In evaluating prediction markets (and other crowd-prediction mechanisms),
investigators have repeatedly observed a so-called "wisdom of crowds" effect,
which roughly says that the average of participants performs much better than
the average participant. The market price---an average or at least aggregate of
traders' beliefs---offers a better estimate than most any individual trader's
opinion. In this paper, we ask a stronger question: how does the market price
compare to the best trader's belief, not just the average trader.

A resistive memory network that has no crossover wiring is proposed to
overcome the hardware limitations to size and functional complexity that is
associated with conventional analogue neural networks. The proposed memory
network is based on simple network cells that are arranged in a hierarchical
modular architecture. Cognitive functionality of this network is demonstrated
by an example of character recognition. The network is trained by an
evolutionary process to completely recognise characters deformed by random
noise, rotation, scaling and shifting

The bi-objective winner determination problem (2WDP-SC) of a combinatorial
procurement auction for transport contracts comes up to a multi-criteria set
covering problem. We are given a set B of bundle bids. A bundle bid b in B
consists of a bidding carrier c_b, a bid price p_b, and a set tau_b of
transport contracts which is a subset of the set T of tendered transport
contracts. Additionally, the transport quality q_t,c_b is given which is
expected to be realized when a transport contract t is executed by a carrier
c_b.

Covering model provides a general framework for granular computing in that
overlapping among granules are almost indispensable. For any given covering,
both intersection and union of covering blocks containing an element are
exploited as granules to form granular worlds at different abstraction levels,
respectively, and transformations among these different granular worlds are
also discussed.

Hierarchical Task Network (HTN) planning uses task decomposition to plan for
an executable sequence of actions as a solution to a problem. In order to
reason effectively, an HTN planner needs expressive domain knowledge. For
instance, a simplified HTN planning system such as JSHOP2 uses such
expressivity and avoids some task interactions due to the increased complexity
of the planning process.

In this paper we present the experimental results of the neural network
control of a servo-system in order to control its speed. The control strategy
is implemented by using an inverse-model control based on Artificial Neural
Networks (ANNs). The network training was performed using two learning
algorithms: Levenberg-Marquardt and Bayesian regularization. We evaluate the
generalization capability for each method according to both the correct
operation of the controller to follow the reference signal, and the control
efforts developed by the ANN-based controller.

The theoretical transition from the graphs of production systems to the
bipartite graphs of the MIVAR nets is shown. Examples of the implementation of
the MIVAR nets in the formalisms of matrixes and graphs are given. The linear
computational complexity of algorithms for automated building of objects and
rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets
the UDAV software complex is developed, handling more than 1.17 million objects
and more than 3.5 million rules on ordinary computers.

Resolution is the rule of inference at the basis of most procedures for
automated reasoning. In these procedures, the input formula is first translated
into an equisatisfiable formula in conjunctive normal form (CNF) and then
represented as a set of clauses. Deduction starts by inferring new clauses by
resolution, and goes on until the empty clause is generated or satisfiability
of the set of clauses is proven, e.g., because no new clauses can be generated.

This paper introduces the SEQ BIN meta-constraint with a polytime algorithm
achieving general- ized arc-consistency according to some properties. SEQ BIN
can be used for encoding counting con- straints such as CHANGE, SMOOTH or
INCREAS- ING NVALUE. For some of these constraints and some of their variants
GAC can be enforced with a time and space complexity linear in the sum of
domain sizes, which improves or equals the best known results of the
literature.

This paper presents a novel L1-norm semi-supervised learning algorithm for
robust image analysis by giving new L1-norm formulation of Laplacian
regularization which is the key step of graph-based semi-supervised learning.
Since our L1-norm Laplacian regularization is defined directly over the
eigenvectors of the normalized Laplacian matrix, we successfully formulate
semi-supervised learning as an L1-norm linear reconstruction problem which can
be effectively solved with sparse coding.

This paper introduces and analyzes a battery of inference models for the
problem of semantic role labeling: one based on constraint satisfaction, and
several strategies that model the inference as a meta-learning problem using
discriminative classifiers. These classifiers are developed with a rich set of
novel features that encode proposition and sentence-level information. To our
knowledge, this is the first work that: (a) performs a thorough analysis of
learning-based inference models for semantic role labeling, and (b) compares
several inference strategies in this context.

We study properties of programs with monotone and convex constraints. We
extend to these formalisms concepts and results from normal logic programming.
They include the notions of strong and uniform equivalence with their
characterizations, tight programs and Fages Lemma, program completion and loop
formulas. Our results provide an abstract account of properties of some recent
extensions of logic programming with aggregates, especially the formalism of
lparse programs.

Efficient representations and solutions for large decision problems with
continuous and discrete variables are among the most important challenges faced
by the designers of automated decision support systems. In this paper, we
describe a novel hybrid factored Markov decision process (MDP) model that
allows for a compact representation of these problems, and a new hybrid
approximate linear programming (HALP) framework that permits their efficient
solutions.

It was recently proved that a sound and complete qualitative simulator does
not exist, that is, as long as the input-output vocabulary of the
state-of-the-art QSIM algorithm is used, there will always be input models
which cause any simulator with a coverage guarantee to make spurious
predictions in its output. In this paper, we examine whether a meaningfully
expressive restriction of this vocabulary is possible so that one can build a
simulator with both the soundness and completeness properties.

A delta-model is a satisfying assignment of a Boolean formula for which any
small alteration, such as a single bit flip, can be repaired by flips to some
small number of other bits, yielding a new satisfying assignment. These
satisfying assignments represent robust solutions to optimization problems
(e.g., scheduling) where it is possible to recover from unforeseen events
(e.g., a resource becoming unavailable). The concept of delta-models was
introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that
finding delta-models for general Boolean formulas is NP-complete.

In recent years, CP-nets have emerged as a useful tool for supporting
preference elicitation, reasoning, and representation. CP-nets capture and
support reasoning with qualitative conditional preference statements,
statements that are relatively natural for users to express. In this paper, we
extend the CP-nets formalism to handle another class of very natural
qualitative statements one often uses in expressing preferences in daily life -
statements of relative importance of attributes.

We present a novel framework for integrating prior knowledge into
discriminative classifiers. Our framework allows discriminative classifiers
such as Support Vector Machines (SVMs) to utilize prior knowledge specified in
the generative setting. The dual objective of fitting the data and respecting
prior knowledge is formulated as a bilevel program, which is solved
(approximately) via iterative application of second-order cone programming.

Multiple sequence alignment (MSA) is a ubiquitous problem in computational
biology. Although it is NP-hard to find an optimal solution for an arbitrary
number of sequences, due to the importance of this problem researchers are
trying to push the limits of exact algorithms further. Since MSA can be cast as
a classical path finding problem, it is attracting a growing number of AI
researchers interested in heuristic search algorithms as a challenge with
actual practical relevance. In this paper, we first review two previous,
complementary lines of research.

In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabulary. An important characteristic of peer-to-peer inference systems
is that the global theory (the union of all peer theories) is not known (as
opposed to partition-based reasoning systems).

Between 1998 and 2004, the planning community has seen vast progress in terms
of the sizes of benchmark examples that domain-independent planners can tackle
successfully. The key technique behind this progress is the use of heuristic
functions based on relaxing the planning task at hand, where the relaxation is
to assume that all delete lists are empty. The unprecedented success of such
methods, in many commonly used benchmark examples, calls for an understanding
of what classes of domains these methods are well suited for.

We discuss an attentional model for simultaneous object tracking and
recognition that is driven by gaze data. Motivated by theories of perception,
the model consists of two interacting pathways: identity and control, intended
to mirror the what and where pathways in neuroscience models. The identity
pathway models object appearance and performs classification using deep
(factored)-Restricted Boltzmann Machines. At each point in time the
observations consist of foveated images, with decaying resolution toward the
periphery of the gaze.

A decision process in which rewards depend on history rather than merely on
the current state is called a decision process with non-Markovian rewards
(NMRDP). In decision-theoretic planning, where many desirable behaviours are
more naturally expressed as properties of execution sequences rather than as
properties of states, NMRDPs form a more natural model than the commonly
adopted fully Markovian decision process (MDP) model.

Tabu search is one of the most effective heuristics for locating high-quality
solutions to a diverse array of NP-hard combinatorial optimization problems.
Despite the widespread success of tabu search, researchers have a poor
understanding of many key theoretical aspects of this algorithm, including
models of the high-level run-time dynamics and identification of those search
space features that influence problem difficulty. We consider these questions
in the context of the job-shop scheduling problem (JSP), a domain where tabu
search algorithms have been shown to be remarkably effective.

The Optiplan planning system is the first integer programming-based planner
that successfully participated in the international planning competition. This
engineering note describes the architecture of Optiplan and provides the
integer programming formulation that enabled it to perform reasonably well in
the competition. We also touch upon some recent developments that make integer
programming encodings significantly more competitive.

Despite recent progress in AI planning, many benchmarks remain challenging
for current planners. In many domains, the performance of a planner can greatly
be improved by discovering and exploiting information about the domain
structure that is not explicitly encoded in the initial PDDL formulation. In
this paper we present and compare two automated methods that learn relevant
information from previous experience in a domain and use it to solve new
problem instances. Our methods share a common four-step strategy.

Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov
models to deal with sequences of structured symbols in the form of logical
atoms, rather than flat characters.

This note formally introduces LOHMMs and presents solutions to the three
central inference problems for LOHMMs: evaluation, most likely hidden state
sequence and parameter estimation. The resulting representation and algorithms
are experimentally evaluated on problems from the domain of bioinformatics.

Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points.

This paper extends the framework of partially observable Markov decision
processes (POMDPs) to multi-agent settings by incorporating the notion of agent
models into the state space. Agents maintain beliefs over physical states of
the environment and over models of other agents, and they use Bayesian updates
to maintain their beliefs over time. The solutions map belief states to
actions. Models of other agents may include their belief states and are related
to agent types considered in games of incomplete information.

We present a uniform non-monotonic solution to the problems of reasoning
about action on the basis of an argumentation-theoretic approach. Our theory is
provably correct relative to a sensible minimisation policy introduced on top
of a temporal propositional logic. Sophisticated problem domains can be
formalised in our framework.

We present a novel approach to the automatic acquisition of taxonomies or
concept hierarchies from a text corpus. The approach is based on Formal Concept
Analysis (FCA), a method mainly used for the analysis of data, i.e. for
investigating and processing explicitly given information. We follow Harris
distributional hypothesis and model the context of a certain term as a vector
representing syntactic dependencies which are automatically acquired from the
text corpus with a linguistic parser.

Variable elimination is a general technique for constraint processing. It is
often discarded because of its high space complexity. However, it can be
extremely useful when combined with other techniques. In this paper we study
the applicability of variable elimination to the challenging problem of finding
still-lifes. We illustrate several alternatives: variable elimination as a
stand-alone algorithm, interleaved with search, and as a source of good quality
lower bounds. We show that these techniques are the best known option both
theoretically and empirically.

Artificial general intelligence (AGI) refers to research aimed at tackling
the full problem of artificial intelligence, that is, create truly intelligent
agents. This sets it apart from most AI research which aims at solving
relatively narrow domains, such as character recognition, motion planning, or
increasing player satisfaction in games. But how do we know when an agent is
truly intelligent?

Publishing private data on external servers incurs the problem of how to
avoid unwanted disclosure of confidential data. We study a problem of
confidentiality in extended disjunctive logic programs and show how it can be
solved by extended abduction. In particular, we analyze how credulous
non-monotonic reasoning affects confidentiality.

In order to give appropriate semantics to qualitative conditionals of the
form "if A then normally B", ordinal conditional functions (OCFs) ranking the
possible worlds according to their degree of plausibility can be used. An OCF
accepting all conditionals of a knowledge base R can be characterized as the
solution of a constraint satisfaction problem. We present a high-level,
declarative approach using constraint logic programming techniques for solving
this constraint satisfaction problem.

Given a set of several inputs into a system (e.g., independent variables
characterizing stimuli) and a set of several stochastically non-independent
outputs (e.g., random variables describing different aspects of responses), how
can one determine, for each of the outputs, which of the inputs it is
influenced by? The problem has applications ranging from modeling pairwise
comparisons to reconstructing mental processing architectures to conjoint
testing.

Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and
etc.) emerged in recent years that allow requesters from all around the world
to post tasks and seek help from an equally global pool of workers. However,
intrinsic incentive problems reside in crowdsourcing applications as workers
and requester are selfish and aim to strategically maximize their own benefit.
In this paper, we propose to provide incentives for workers to exert effort
using a novel game-theoretic model based on repeated games.

Commute Time Distance (CTD) is a random walk based metric on graphs. CTD has
found widespread applications in many domains including personalized search,
collaborative filtering and making search engines robust against manipulation.
Our interest is inspired by the use of CTD as a metric for anomaly detection.
It has been shown that CTD can be used to simultaneously identify both global
and local anomalies. Here we propose an accurate and efficient approximation
for computing the CTD in an incremental fashion in order to facilitate
real-time applications.

The superiority and inferiority ranking (SIR) method is a generation of the
well-known PROMETHEE method, which can be more efficient to deal with
multi-criterion decision making (MCDM) problem. Intuitionistic fuzzy sets
(IFSs), as an important extension of fuzzy sets (IFs), include both membership
functions and non-membership functions and can be used to, more precisely
describe uncertain information. In real world, decision situations are usually
under uncertain environment and involve multiple individuals who have their own
points of view on handing of decision problems.

We propose a model for errors in sung queries, a variant of the hidden Markov
model (HMM). This is a solution to the problem of identifying the degree of
similarity between a (typically error-laden) sung query and a potential target
in a database of musical works, an important problem in the field of music
information retrieval. Similarity metrics are a critical component of
query-by-humming (QBH) applications which search audio and multimedia databases
for strong matches to oral queries.

Decentralized control of cooperative systems captures the operation of a
group of decision makers that share a single global objective. The difficulty
in solving optimally such problems arises when the agents lack full
observability of the global state of the system when they operate. The general
problem has been shown to be NEXP-complete. In this paper, we identify classes
of decentralized control problems whose complexity ranges between NEXP and P.
In particular, we study problems characterized by independent transitions,
independent observations, and goal-oriented objective functions.

We explore a method for computing admissible heuristic evaluation functions
for search problems. It utilizes pattern databases, which are precomputed
tables of the exact cost of solving various subproblems of an existing problem.
Unlike standard pattern database heuristics, however, we partition our problems
into disjoint subproblems, so that the costs of solving the different
subproblems can be added together without overestimating the cost of solving
the original problem.

Many known planning tasks have inherent constraints concerning the best order
in which to achieve the goals. A number of research efforts have been made to
detect such constraints and to use them for guiding search, in the hope of
speeding up the planning process. We go beyond the previous approaches by
considering ordering constraints not only over the (top-level) goals, but also
over the sub-goals that will necessarily arise during planning. Landmarks are
facts that must be true at some point in every valid solution plan.

Many researchers in artificial intelligence are beginning to explore the use
of soft constraints to express a set of (possibly conflicting) problem
requirements. A soft constraint is a function defined on a collection of
variables which associates some measure of desirability with each possible
combination of values for those variables. However, the crucial question of the
computational complexity of finding the optimal solution to a collection of
soft constraints has so far received very little attention.

Argumentation is based on the exchange and valuation of interacting
arguments, followed by the selection of the most acceptable of them (for
example, in order to take a decision, to make a choice).

We address the problem of finding the shortest path between two points in an
unknown real physical environment, where a traveling agent must move around in
the environment to explore unknown territory. We introduce the Physical-A*
algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes
that A* would expand and returns the shortest path between the two points.
However, due to the physical nature of the problem, the complexity of the
algorithm is measured by the traveling effort of the moving agent and not by
the number of generated nodes, as in standard A*.

When writing a constraint program, we have to choose which variables should
be the decision variables, and how to represent the constraints on these
variables. In many cases, there is considerable choice for the decision
variables. Consider, for example, permutation problems in which we have as many
values as variables, and each variable takes an unique value. In such problems,
we can choose between a primal and a dual viewpoint. In the dual viewpoint,
each dual variable represents one of the primal values, whilst each dual value
represents one of the primal variables.

A novel algorithm for actively trading stocks is presented. While traditional
expert advice and "universal" algorithms (as well as standard technical trading
heuristics) attempt to predict winners or trends, our approach relies on
predictable statistical relations between all pairs of stocks in the market.
Our empirical results on historical markets provide strong evidence that this
type of technical trading can "beat the market" and moreover, can beat the best
stock in the market. In doing so we utilize a new idea for smoothing critical
parameters in the context of expert learning.

The predominant knowledge-based approach to automated model construction,
compositional modelling, employs a set of models of particular functional
components. Its inference mechanism takes a scenario describing the constituent
interacting components of a system and translates it into a useful mathematical
model. This paper presents a novel compositional modelling approach aimed at
building model repositories. It furthers the field in two respects.

We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases.

We propose a formalism for representation of finite languages, referred to as
the class of IDL-expressions, which combines concepts that were only considered
in isolation in existing formalisms. The suggested applications are in natural
language processing, more specifically in surface natural language generation
and in machine translation, where a sentence is obtained by first generating a
large set of candidate sentences, represented in a compact way, and then by
filtering such a set through a parser.

Supply chain formation is the process of determining the structure and terms
of exchange relationships to enable a multilevel, multiagent production
activity. We present a simple model of supply chains, highlighting two
characteristic features: hierarchical subtask decomposition, and resource
contention. To decentralize the formation process, we introduce a market price
system over the resources produced along the chain. In a competitive
equilibrium for this system, agents choose locally optimal allocations with
respect to prices, and outcomes are optimal overall.

MAP is the problem of finding a most probable instantiation of a set of
variables given evidence. MAP has always been perceived to be significantly
harder than the related problems of computing the probability of a variable
instantiation Pr, or the problem of computing the most probable explanation
(MPE). This paper investigates the complexity of MAP in Bayesian networks.
Specifically, we show that MAP is complete for NP^PP and provide further
negative complexity results for algorithms based on variable elimination. We
also show that MAP remains hard even when MPE and Pr become easy.

Although many algorithms have been designed to construct Bayesian network
structures using different approaches and principles, they all employ only two
methods: those based on independence criteria, and those based on a scoring
function and a search procedure (although some methods combine the two). Within
the score+search paradigm, the dominant approach uses local search methods in
the space of directed acyclic graphs (DAGs), where the usual choices for
defining the elementary modifications (local changes) that can be applied are
arc addition, arc deletion, and arc reversal.

Independence -- the study of what is relevant to a given problem of reasoning
-- has received an increasing attention from the AI community. In this paper,
we consider two basic forms of independence, namely, a syntactic one and a
semantic one. We show features and drawbacks of them. In particular, while the
syntactic form of independence is computationally easy to check, there are
cases in which things that intuitively are not relevant are not recognized as
such.

Adjustable autonomy refers to entities dynamically varying their own
autonomy, transferring decision-making control to other entities (typically
agents transferring control to human users) in key situations. Determining
whether and when such transfers-of-control should occur is arguably the
fundamental research problem in adjustable autonomy. Previous work has
investigated various approaches to addressing this problem but has often
focused on individual agent-human interactions.

We develop, analyze, and evaluate a novel, supervised, specific-to-general
learner for a simple temporal logic and use the resulting algorithm to learn
visual event definitions from video sequences. First, we introduce a simple,
propositional, temporal, event-description language called AMA that is
sufficiently expressive to represent many events yet sufficiently restrictive
to support learning. We then give algorithms, along with lower and upper
complexity bounds, for the subsumption and generalization problems for AMA
formulas.

In recent years research in the planning community has moved increasingly
toward s application of planners to realistic problems involving both time and
many typ es of resources. For example, interest in planning demonstrated by the
space res earch community has inspired work in observation scheduling,
planetary rover ex ploration and spacecraft control domains.

Prediction markets show considerable promise for developing flexible
mechanisms for machine learning. Here, machine learning markets for
multivariate systems are defined, and a utility-based framework is established
for their analysis. This differs from the usual approach of defining static
betting functions. It is shown that such markets can implement model
combination methods used in machine learning, such as product of expert and
mixture of expert approaches as equilibrium pricing models, by varying agent
utility functions.

This preliminary report addresses the expressive power of unit resolution
regarding input data encoded with partial truth assignments of propositional
variables. A characterization of the functions that are computable in this way,
which we propose to call propagatable functions, is given. By establishing that
propagatable functions can also be computed using monotone circuits, we show
that there exist polynomial time complexity propagable functions requiring an
exponential amount of clauses to be computed using unit resolution.

Many widely studied graphical models with latent variables lead to nontrivial
constraints on the distribution of the observed variables. Inspired by the Bell
inequalities in quantum mechanics, we refer to any linear inequality whose
violation rules out some latent variable model as a "hidden variable test" for
that model. Our main contribution is to introduce a sequence of relaxations
which provides progressively tighter hidden variable tests. We demonstrate
applicability to mixtures of sequences of i.i.d. variables, Bell inequalities,
and homophily models in social networks.

We introduce a temporal model for reasoning on disjunctive metric constraints
on intervals and time points in temporal contexts. This temporal model is
composed of a labeled temporal algebra and its reasoning algorithms. The
labeled temporal algebra defines labeled disjunctive metric point-based
constraints, where each disjunct in each input disjunctive constraint is
univocally associated to a label. Reasoning algorithms manage labeled
constraints, associated label lists, and sets of mutually inconsistent
disjuncts.

We introduce the Xapagy cognitive architecture: a software system designed to
perform narrative reasoning. The architecture has been designed from scratch to
model and mimic the activities performed by humans when witnessing, reading,
recalling, narrating and talking about stories.

The theory revision problem is the problem of how best to go about revising a
deficient domain theory using information contained in examples that expose
inaccuracies. In this paper we present our approach to the theory revision
problem for propositional domain theories. The approach described here, called
PTR, uses probabilities associated with domain theory elements to numerically
track the "flow" of proof through the theory. This allows us to measure the
precise role of a clause or literal in allowing or preventing a (desired or
undesired) derivation for a given example.

This paper describes a graph clustering algorithm that aims to minimize the
normalized cut criterion and has a model order selection procedure. The
performance of the proposed algorithm is comparable to spectral approaches in
terms of minimizing normalized cut. However, unlike spectral approaches, the
proposed algorithm scales to graphs with millions of nodes and edges. The
algorithm consists of three components that are processed sequentially: a
greedy agglomerative hierarchical clustering procedure, model order selection,
and a local refinement.

We solve constraint satisfaction problems through translation to answer set
programming (ASP). Our reformulations have the property that unit-propagation
in the ASP solver achieves well defined local consistency properties like arc,
bound and range consistency. Experiments demonstrate the computational value of
this approach.

We explore self-organizing strategies for role assignment in a foraging task
carried out by a colony of artificial agents. Our strategies are inspired by
various mechanisms of division of labor (polyethism) observed in eusocial
insects like ants, termites, or bees. Specifically we instantiate models of
caste polyethism and age or temporal polyethism to evaluated the benefits to
foraging in a dynamic environment. Our experiment is directly related to the
exploration/exploitation trade of in machine learning.

A definition of intelligence is given in terms of performance that can be
quantitatively measured. In this study, we have presented a conceptual model of
Intelligent Agent System for Automatic Vehicle Checking Agent (VCA). To achieve
this goal, we have introduced several kinds of agents that exhibit intelligent
features. These are the Management agent, internal agent, External Agent,
Watcher agent and Report agent. Metrics and measurements are suggested for
evaluating the performance of Automatic Vehicle Checking Agent (VCA).

To maximize its success, an AGI typically needs to explore its initially
unknown world. Is there an optimal way of doing so? Here we derive an
affirmative answer for a broad class of environments.

Natural Immune system plays a vital role in the survival of the all living
being. It provides a mechanism to defend itself from external predates making
it consistent systems, capable of adapting itself for survival incase of
changes. The human immune system has motivated scientists and engineers for
finding powerful information processing algorithms that has solved complex
engineering tasks. This paper explores one of the various possibilities for
solving problem in a Multiagent scenario wherein multiple robots are deployed
to achieve a goal collectively.

The process-based semantic composition of Web Services is gaining a
considerable momentum as an approach for the effective integration of
distributed, heterogeneous, and autonomous applications. To compose Web
Services semantically, we need an ontology. There are several ways of inserting
semantics in Web Services. One of them consists of using description languages
like OWL-S. In this paper, we introduce our work which consists in the
proposition of a new model and the use of semantic matching technology for
semantic and dynamic composition of ebXML business processes.

Rules in logic programming encode information about mutual interdependencies
between literals that is not captured by any of the commonly used semantics.
This information becomes essential as soon as a program needs to be modified or
further manipulated.

The ability to predict the intentions of people based solely on their visual
actions is a skill only performed by humans and animals. The intelligence of
current computer algorithms has not reached this level of complexity, but there
are several research efforts that are working towards it. With the number of
classification algorithms available, it is hard to determine which algorithm
works best for a particular situation. In classification of visual human intent
data, Hidden Markov Models (HMM), and their variants, are leading candidates.

This work introduces the IB-score, a family of independence-based score
functions for robust learning of Markov networks independence structures.
Markov networks are a widely used graphical representation of probability
distributions, with many applications in several fields of science. The main
advantage of the IB-score is the possibility of computing it without the need
of estimation of the numerical parameters, an NP-hard problem, usually solved
through an approximate, data-intensive, iterative optimization.

In this report, we propose a quick survey of the currently known techniques
for encoding a Boolean cardinality constraint into a CNF formula, and we
discuss about the relevance of these encodings. We also propose models to
facilitate analysis and design of CNF encodings for Boolean constraints.

There has been a long history of using fuzzy language equivalence to compare
the behavior of fuzzy systems, but the comparison at this level is too coarse.
Recently, a finer behavioral measure, bisimulation, has been introduced to
fuzzy finite automata. However, the results obtained are applicable only to
finite-state systems. In this paper, we consider bisimulation for general fuzzy
systems which may be infinite-state or infinite-event, by modeling them as
fuzzy transition systems.

Research in the Life Sciences depends on the integration of large,
distributed and heterogeneous data sources and web services. The discovery of
which of these resources are the most appropriate to solve a given task is a
complex research question, since there is a large amount of plausible
candidates and there is little, mostly unstructured, metadata to be able to
decide among them.We contribute a semi-automatic approach,based on semantic
techniques, to assist researchers in the discovery of the most appropriate web
services to full a set of given requirements.

In this paper we present a preliminary logic-based evaluation of the
integration of post-composed phenotypic descriptions with domain ontologies.
The evaluation has been performed using a description logic reasoner together
with scalable techniques: ontology modularization and approximations of the
logical difference between ontologies.

This research applies ideas from argumentation theory in the context of
semantic wikis, aiming to provide support for structured-large scale
argumentation between human agents. The implemented prototype is exemplified by
modelling the MMR vaccine controversy.

Our work concerns the elucidation of the cancer (epi)genome, transcriptome
and proteome to better understand the complex interplay between a cancer cell's
molecular state and its response to anti-cancer therapy. To study the problem,
we have previously focused on data warehousing technologies and statistical
data integration. In this paper, we present recent work on extending our
analytical capabilities using Semantic Web technology. A key new component
presented here is a SPARQL endpoint to our existing data warehouse.

In this paper we dealt with the comparison and linking between lexical
resources with domain knowledge provided by ontologies. It is one of the issues
for the combination of the Semantic Web Ontologies and Text Mining. We
investigated the relations between the linguistics oriented and domain-specific
semantics, by associating the GO biological process concepts to the FrameNet
semantic frames. The result shows the gaps between the linguistics-oriented and
domain-specific semantics on the classification of events and the grouping of
target words.

Uncertainty of decisions in safety-critical engineering applications can be
estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC)
technique of averaging over decision models. The use of decision tree (DT)
models assists experts to interpret causal relations and find factors of the
uncertainty. Bayesian averaging also allows experts to estimate the uncertainty
accurately when a priori information on the favored structure of DTs is
available. Then an expert can select a single DT model, typically the Maximum a
Posteriori model, for interpretation purposes.

We present a probabilistic logic programming framework to reinforcement
learning, by integrating reinforce-ment learning, in POMDP environments, with
normal hybrid probabilistic logic programs with probabilistic answer set
seman-tics, that is capable of representing domain-specific knowledge. We
formally prove the correctness of our approach. We show that the complexity of
finding a policy for a reinforcement learning problem in our approach is
NP-complete. In addition, we show that any reinforcement learning problem can
be encoded as a classical logic program with answer set semantics.

Our work has focused on support for film or television scriptwriting. Since
this involves potentially varied story-lines, we note the implicit or latent
support for interactivity. Furthermore the film, television, games, publishing
and other sectors are converging, so that cross-over and re-use of one form of
product in another of these sectors is ever more common. Technically our work
has been largely based on mathematical algorithms for data clustering and
display. Operationally, we also discuss how our algorithms can support
collective, distributed problem-solving.

The task of verifying the compatibility between interacting web services has
traditionally been limited to checking the compatibility of the interaction
protocol in terms of message sequences and the type of data being exchanged.
Since web services are developed largely in an uncoordinated way, different
services often use independently developed ontologies for the same domain
instead of adhering to a single ontology as standard.

The aim of this study is to show the importance of two classification
techniques, viz. decision tree and clustering, in prediction of learning
disabilities (LD) of school-age children. LDs affect about 10 percent of all
children enrolled in schools. The problems of children with specific learning
disabilities have been a cause of concern to parents and teachers for some
time. Decision trees and clustering are powerful and popular tools used for
classification and prediction in Data mining. Different rules extracted from
the decision tree are used for prediction of learning disabilities.

Substitutability, interchangeability and related concepts in Constraint
Programming were introduced approximately twenty years ago and have given rise
to considerable subsequent research. We survey this work, classify, and relate
the different concepts, and indicate directions for future work, in particular
with respect to making connections with research into symmetry breaking. This
paper is a condensed version of a larger work in progress.

DCSP (Distributed Constraint Satisfaction Problem) has been a very important
research area in AI (Artificial Intelligence). There are many application
problems in distributed AI that can be formalized as DSCPs. With the increasing
complexity and problem size of the application problems in AI, the required
storage place in searching and the average searching time are increasing too.
Thus, to use a limited storage place efficiently in solving DCSP becomes a very
important problem, and it can help to reduce searching time as well.

Bangla alphabet has a large number of letters, for this it is complicated to
type faster using Bangla keyboard. The proposed keyboard will maximize the
speed of operator as they can type with both hands parallel. Association rule
of data mining to distribute the Bangla characters in the keyboard is used
here.

In this paper we present an optimal Bangla Keyboard Layout, which distributes
the load equally on both hands so that maximizing the ease and minimizing the
effort. Bangla alphabet has a large number of letters, for this it is difficult
to type faster using Bangla keyboard. Our proposed keyboard will maximize the
speed of operator as they can type with both hands parallel. Here we use the
association rule of data mining to distribute the Bangla characters in the
keyboard.

Symmetry is a common feature of many combinatorial problems. Unfortunately
eliminating all symmetry from a problem is often computationally intractable.
This paper argues that recent parameterized complexity results provide insight
into that intractability and help identify special cases in which symmetry can
be dealt with more tractably

The technical report presents a generic exact solution approach for
minimizing the project duration of the resource-constrained project scheduling
problem with generalized precedences (Rcpsp/max). The approach uses lazy clause
generation, i.e., a hybrid of finite domain and Boolean satisfiability solving,
in order to apply nogood learning and conflict-driven search on the solution
generation. Our experiments show the benefit of lazy clause generation for
finding an optimal solutions and proving its optimality in comparison to other
state-of-the-art exact and non-exact methods.

In many applications involving multi-media data, the definition of similarity
between items is integral to several key tasks, e.g., nearest-neighbor
retrieval, classification, and recommendation. Data in such regimes typically
exhibits multiple modalities, such as acoustic and visual content of video.
Integrating such heterogeneous data to form a holistic similarity space is
therefore a key challenge to be overcome in many real-world applications.

We focus on credal nets, which are graphical models that generalise Bayesian
nets to imprecise probability. We replace the notion of strong independence
commonly used in credal nets with the weaker notion of epistemic irrelevance,
which is arguably more suited for a behavioural theory of probability. Focusing
on directed trees, we show how to combine the given local uncertainty models in
the nodes of the graph into a global model, and we use this to construct and
justify an exact message-passing algorithm that computes updated beliefs for a
variable in the tree.

World Wide Web (WWW) is the most popular global information sharing and
communication system consisting of three standards .i.e., Uniform Resource
Identifier (URL), Hypertext Transfer Protocol (HTTP) and Hypertext Mark-up
Language (HTML). Information is provided in text, image, audio and video
formats over the web by using HTML which is considered to be unconventional in
defining and formalizing the meaning of the context...

Web development is a challenging research area for its creativity and
complexity. The existing raised key challenge in web technology technologic
development is the presentation of data in machine read and process able format
to take advantage in knowledge based information extraction and maintenance.
Currently it is not possible to search and extract optimized results using full
text queries because there is no such mechanism exists which can fully extract
the semantic from full text queries and then look for particular knowledge
based information.

The two standard branching schemes for CSPs are d-way and 2-way branching.
Although it has been shown that in theory the latter can be exponentially more
effective than the former, there is a lack of empirical evidence showing such
differences. To investigate this, we initially make an experimental comparison
of the two branching schemes over a wide range of benchmarks. Experimental
results verify the theoretical gap between d-way and 2-way branching as we move
from a simple variable ordering heuristic like dom to more sophisticated ones
like dom/ddeg.

The paper investigates a novel approach, based on Constraint Logic
Programming (CLP), to predict the 3D conformation of a protein via fragments
assembly. The fragments are extracted by a preprocessor-also developed for this
work- from a database of known protein structures that clusters and classifies
the fragments according to similarity and frequency. The problem of assembling
fragments into a complete conformation is mapped to a constraint solving
problem and solved using CLP.

The stable marriage problem is a well-known problem of matching men to women
so that no man and woman, who are not married to each other, both prefer each
other. Such a problem has a wide variety of practical applications, ranging
from matching resident doctors to hospitals, to matching students to schools or
more generally to any two-sided market. In the classical stable marriage
problem, both men and women express a strict preference order over the members
of the other sex, in a qualitative way.

We study the problem of coalitional manipulation in elections using the
unweighted Borda rule. We provide empirical evidence of the manipulability of
Borda elections in the form of two new greedy manipulation algorithms based on
intuitions from the bin-packing and multiprocessor scheduling domains.

This paper models a decision support system to predict the occurance of
suicide attack in a given collection of cities. The system comprises two parts.
First part analyzes and identifies the factors which affect the prediction.
Admitting incomplete information and use of linguistic terms by experts, as two
characteristic features of this peculiar prediction problem we exploit the
Theory of Fuzzy Soft Sets.

Hybrid MKNF knowledge bases are one of the most prominent tightly integrated
combinations of open-world ontology languages with closed-world (non-monotonic)
rule paradigms. The definition of Hybrid MKNF is parametric on the ontology
language, in the sense that non-monotonic rules can extend any decidable
ontology language. Hybrid MKNF has also been defined for rules that are
evaluated under the stable model semantics and under the well-founded semantics
(WFS).

The idea of the Semantic Web is to annotate Web content and services with
computer interpretable descriptions with the aim to automatize many tasks
currently performed by human users. In the context of Web services, one of the
most interesting tasks is their composition. In this paper we formalize this
problem in the framework of a constructive description logic. In particular we
propose a declarative service specification language and a calculus for service
composition.

The stable marriage (SM) problem has a wide variety of practical
applications, ranging from matching resident doctors to hospitals, to matching
students to schools, or more generally to any two-sided market. In the
classical formulation, n men and n women express their preferences (via a
strict total order) over the members of the other sex. Solving a SM problem
means finding a stable marriage where stability is an envy-free notion: no man
and woman who are not married to each other would both prefer each other to
their partners or to being single.

Notions of core, support and inversion of a soft set have been de ned and
studied. Soft approximations are soft sets developed through core and support,
and are used for granulating the soft space. Membership structure of a soft set
has been probed in and many interesting properties presented. The mathematical
apparatus developed so far in this paper yields a detailed analysis of two
works viz. [N. Cagman, S. Enginoglu, Soft set theory and uni-int decision
making, European Jr. of Operational Research (article in press, available
online 12 May 2010)] and [N. Cagman, S.

Frequent Episode Discovery framework is a popular framework in Temporal Data
Mining with many applications. Over the years many different notions of
frequencies of episodes have been proposed along with different algorithms for
episode discovery. In this paper we present a unified view of all such
frequency counting algorithms. We present a generic algorithm such that all
current algorithms are special cases of it. This unified view allows one to
gain insights into different frequencies and we present quantitative
relationships among different frequencies.

Symmetry can be used to help solve many problems. For instance, Einstein's
famous 1905 paper ("On the Electrodynamics of Moving Bodies") uses symmetry to
help derive the laws of special relativity. In artificial intelligence,
symmetry has played an important role in both problem representation and
reasoning. I describe recent work on using symmetry to help solve constraint
satisfaction problems. Symmetries occur within individual solutions of problems
as well as between different solutions of the same problem.

We consider a common type of symmetry where we have a matrix of decision
variables with interchangeable rows and columns. A simple and efficient method
to deal with such row and column symmetry is to post symmetry breaking
constraints like DOUBLELEX and SNAKELEX. We provide a number of positive and
negative results on posting such symmetry breaking constraints.

Circumscription is a representative example of a nonmonotonic reasoning
inference technique. Circumscription has often been studied for first order
theories, but its propositional version has also been the subject of extensive
research, having been shown equivalent to extended closed world assumption
(ECWA).

The Dendritic Cell Algorithm (DCA) is inspired by the function of the
dendritic cells of the human immune system. In nature, dendritic cells are the
intrusion detection agents of the human body, policing the tissue and organs
for potential invaders in the form of pathogens. In this research, and abstract
model of DC behaviour is developed and subsequently used to form an algorithm,
the DCA.

Functional constraints and bi-functional constraints are an important
constraint class in Constraint Programming (CP) systems, in particular for
Constraint Logic Programming (CLP) systems. CP systems with finite domain
constraints usually employ CSP-based solvers which use local consistency, for
example, arc consistency. We introduce a new approach which is based instead on
variable substitution. We obtain efficient algorithms for reducing systems
involving functional and bi-functional constraints together with other
non-functional constraints.

In order to get strategic positioning for competition in business
organization, the information system must be ahead in this information age
where the information as one of the weapons to win the competition and in the
right hand the information will become a right bullet. The information system
with the information technology support isn't enough if just only on internet
or implemented with internet technology.

After Aceh's quake many earthquakes have struck Indonesia alternately and
even other disasters have been a threat for every citizen in this country.
Actually an everyday occurrence on earth and more than 3 million earthquakes
occur every year, about 8,000 a day, or one every 11 seconds in Indonesia there
are 5 to 30 quakes prediction everyday. Government's responsibility to protect
the citizen has been done by making National body of disaster management.
Preparing, saving and distribution logistic become National body of disaster
management's responsibility to build information management.

Class prediction is an important application of microarray gene expression
data analysis. The high-dimensionality of microarray data, where number of
genes (variables) is very large compared to the number of samples (obser-
vations), makes the application of many prediction techniques (e.g., logistic
regression, discriminant analysis) difficult. An efficient way to solve this
prob- lem is by using dimension reduction statistical techniques. Increasingly
used in psychology-related applications, Rasch model (RM) provides an appealing
framework for handling high-dimensional microarray data.

In this paper, the performances of the quasi-Newton BFGS algorithm, the
NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution
Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm
Optimizers (PSO) are compared experimentally on benchmark functions reflecting
important challenges encountered in real-world optimization problems.
Dependence of the performances in the conditioning of the problem and
rotational invariance of the algorithms are in particular investigated.

Collaborative tagging systems, such as Delicious, CiteULike, and others,
allow users to annotate resources, e.g., Web pages or scientific papers, with
descriptive labels called tags. The social annotations contributed by thousands
of users, can potentially be used to infer categorical knowledge, classify
documents or recommend new relevant information. Traditional text inference
methods do not make best use of social annotation, since they do not take into
account variations in individual users' perspectives and vocabulary.

Structured and semi-structured data describing entities, taxonomies and
ontologies appears in many domains. There is a huge interest in integrating
structured information from multiple sources; however integrating structured
data to infer complex common structures is a difficult task because the
integration must aggregate similar structures while avoiding structural
inconsistencies that may appear when the data is combined.

In logic there is a clear concept of what constitutes a proof and what not. A
proof is essentially defined as a finite sequence of formulae which are either
axioms or derived by proof rules from formulae earlier in the sequence.
Sociologically, however, it is more difficult to say what should constitute a
proof and what not. In this paper we will look at different forms of proofs and
try to clarify the concept of proof in the wider meaning of the term. This has
implications on how proofs should be represented formally.

In this paper, a new learning algorithm for adaptive network intrusion
detection using naive Bayesian classifier and decision tree is presented, which
performs balance detections and keeps false positives at acceptable level for
different types of network attacks, and eliminates redundant attributes as well
as contradictory examples from training data that make the detection model
complex. The proposed algorithm also addresses some difficulties of data mining
such as handling continuous attribute, dealing with missing attribute values,
and reducing noise in training data.

In the last two decades, a number of methods have been proposed for
forecasting based on fuzzy time series. Most of the fuzzy time series methods
are presented for forecasting of car road accidents. However, the forecasting
accuracy rates of the existing methods are not good enough. In this paper, we
compared our proposed new method of fuzzy time series forecasting with existing
methods. Our method is based on means based partitioning of the historical data
of car road accidents. The proposed method belongs to the kth order and
time-variant methods.