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19,302 | An Explanation Mechanism for Bayesian Inferencing Systems | cs.AI | Explanation facilities are a particularly important feature of expert system
frameworks. It is an area in which traditional rule-based expert system
frameworks have had mixed results. While explanations about control are well
handled, facilities are needed for generating better explanations concerning
knowledge base co... | computer science |
19,303 | Distributed Revision of Belief Commitment in Multi-Hypothesis
Interpretations | cs.AI | This paper extends the applications of belief-networks to include the
revision of belief commitments, i.e., the categorical acceptance of a subset of
hypotheses which, together, constitute the most satisfactory explanation of the
evidence at hand. A coherent model of non-monotonic reasoning is established
and distribut... | computer science |
19,304 | Learning Link-Probabilities in Causal Trees | cs.AI | A learning algorithm is presented which given the structure of a causal tree,
will estimate its link probabilities by sequential measurements on the leaves
only. Internal nodes of the tree represent conceptual (hidden) variables
inaccessible to observation. The method described is incremental, local,
efficient, and rem... | computer science |
19,305 | Approximate Deduction in Single Evidential Bodies | cs.AI | Results on approximate deduction in the context of the calculus of evidence
of Dempster-Shafer and the theory of interval probabilities are reported.
Approximate conditional knowledge about the truth of conditional propositions
was assumed available and expressed as sets of possible values (actually
numeric intervals) ... | computer science |
19,306 | The Rational and Computational Scope of Probabilistic Rule-Based Expert
Systems | cs.AI | Belief updating schemes in artificial intelligence may be viewed as three
dimensional languages, consisting of a syntax (e.g. probabilities or certainty
factors), a calculus (e.g. Bayesian or CF combination rules), and a semantics
(i.e. cognitive interpretations of competing formalisms). This paper studies
the rational... | computer science |
19,307 | A Causal Bayesian Model for the Diagnosis of Appendicitis | cs.AI | The causal Bayesian approach is based on the assumption that effects (e.g.,
symptoms) that are not conditionally independent with respect to some causal
agent (e.g., a disease) are conditionally independent with respect to some
intermediate state caused by the agent, (e.g., a pathological condition). This
paper describ... | computer science |
19,308 | A Backwards View for Assessment | cs.AI | Much artificial intelligence research focuses on the problem of deducing the
validity of unobservable propositions or hypotheses from observable evidence.!
Many of the knowledge representation techniques designed for this problem
encode the relationship between evidence and hypothesis in a directed manner.
Moreover, th... | computer science |
19,309 | DAVID: Influence Diagram Processing System for the Macintosh | cs.AI | Influence diagrams are a directed graph representation for uncertainties as
probabilities. The graph distinguishes between those variables which are under
the control of a decision maker (decisions, shown as rectangles) and those
which are not (chances, shown as ovals), as well as explicitly denoting a goal
for solutio... | computer science |
19,310 | Propagation of Belief Functions: A Distributed Approach | cs.AI | In this paper, we describe a scheme for propagating belief functions in
certain kinds of trees using only local computations. This scheme generalizes
the computational scheme proposed by Shafer and Logan1 for diagnostic trees of
the type studied by Gordon and Shortliffe, and the slightly more general scheme
given by Sh... | computer science |
19,311 | Appropriate and Inappropriate Estimation Techniques | cs.AI | Mode {also called MAP} estimation, mean estimation and median estimation are
examined here to determine when they can be safely used to derive {posterior)
cost minimizing estimates. (These are all Bayes procedures, using the mode.
mean. or median of the posterior distribution). It is found that modal
estimation only re... | computer science |
19,312 | Estimating Uncertain Spatial Relationships in Robotics | cs.AI | In this paper, we describe a representation for spatial information, called
the stochastic map, and associated procedures for building it, reading
information from it, and revising it incrementally as new information is
obtained. The map contains the estimates of relationships among objects in the
map, and their uncert... | computer science |
19,313 | A VLSI Design and Implementation for a Real-Time Approximate Reasoning | cs.AI | The role of inferencing with uncertainty is becoming more important in
rule-based expert systems (ES), since knowledge given by a human expert is
often uncertain or imprecise. We have succeeded in designing a VLSI chip which
can perform an entire inference process based on fuzzy logic. The design of the
VLSI fuzzy infe... | computer science |
19,314 | A General Purpose Inference Engine for Evidential Reasoning Research | cs.AI | The purpose of this paper is to report on the most recent developments in our
ongoing investigation of the representation and manipulation of uncertainty in
automated reasoning systems. In our earlier studies (Tong and Shapiro, 1985) we
described a series of experiments with RUBRIC (Tong et al., 1985), a system for
ful... | computer science |
19,315 | Generalizing Fuzzy Logic Probabilistic Inferences | cs.AI | Linear representations for a subclass of boolean symmetric functions selected
by a parity condition are shown to constitute a generalization of the linear
constraints on probabilities introduced by Boole. These linear constraints are
necessary to compute probabilities of events with relations between the.
arbitrarily s... | computer science |
19,316 | Qualitative Probabilistic Networks for Planning Under Uncertainty | cs.AI | Bayesian networks provide a probabilistic semantics for qualitative
assertions about likelihood. A qualitative reasoner based on an algebra over
these assertions can derive further conclusions about the influence of actions.
While the conclusions are much weaker than those computed from complete
probability distributio... | computer science |
19,317 | Experimentally Comparing Uncertain Inference Systems to Probability | cs.AI | This paper examines the biases and performance of several uncertain inference
systems: Mycin, a variant of Mycin. and a simplified version of probability
using conditional independence assumptions. We present axiomatic arguments for
using Minimum Cross Entropy inference as the best way to do uncertain
inference. For My... | computer science |
19,318 | Evaluation of Uncertain Inference Models I: PROSPECTOR | cs.AI | This paper examines the accuracy of the PROSPECTOR model for uncertain
reasoning. PROSPECTOR's solutions for a large number of computer-generated
inference networks were compared to those obtained from probability theory and
minimum cross-entropy calculations. PROSPECTOR's answers were generally
accurate for a restrict... | computer science |
19,319 | On Implementing Usual Values | cs.AI | In many cases commonsense knowledge consists of knowledge of what is usual.
In this paper we develop a system for reasoning with usual information. This
system is based upon the fact that these pieces of commonsense information
involve both a probabilistic aspect and a granular aspect. We implement this
system with the... | computer science |
19,320 | On the Combinality of Evidence in the Dempster-Shafer Theory | cs.AI | In the current versions of the Dempster-Shafer theory, the only essential
restriction on the validity of the rule of combination is that the sources of
evidence must be statistically independent. Under this assumption, it is
permissible to apply the Dempster-Shafer rule to two or mere distinct
probability distributions... | computer science |
19,321 | Logical Probability Preferences | cs.AI | We present a unified logical framework for representing and reasoning about
both probability quantitative and qualitative preferences in probability answer
set programming, called probability answer set optimization programs. The
proposed framework is vital to allow defining probability quantitative
preferences over th... | computer science |
19,322 | From Constraints to Resolution Rules, Part I: Conceptual Framework | cs.AI | Many real world problems naturally appear as constraints satisfaction
problems (CSP), for which very efficient algorithms are known. Most of these
involve the combination of two techniques: some direct propagation of
constraints between variables (with the goal of reducing their sets of possible
values) and some kind o... | computer science |
19,323 | From Constraints to Resolution Rules, Part II: chains, braids,
confluence and T&E | cs.AI | In this Part II, we apply the general theory developed in Part I to a
detailed analysis of the Constraint Satisfaction Problem (CSP). We show how
specific types of resolution rules can be defined. In particular, we introduce
the general notions of a chain and a braid. As in Part I, these notions are
illustrated in deta... | computer science |
19,324 | An Inequality Paradigm for Probabilistic Knowledge | cs.AI | We propose an inequality paradigm for probabilistic reasoning based on a
logic of upper and lower bounds on conditional probabilities. We investigate a
family of probabilistic logics, generalizing the work of Nilsson [14]. We
develop a variety of logical notions for probabilistic reasoning, including
soundness, complet... | computer science |
19,325 | Probabilistic Interpretations for MYCIN's Certainty Factors | cs.AI | This paper examines the quantities used by MYCIN to reason with uncertainty,
called certainty factors. It is shown that the original definition of certainty
factors is inconsistent with the functions used in MYCIN to combine the
quantities. This inconsistency is used to argue for a redefinition of certainty
factors in ... | computer science |
19,326 | Uncertain Reasoning Using Maximum Entropy Inference | cs.AI | The use of maximum entropy inference in reasoning with uncertain information
is commonly justified by an information-theoretic argument. This paper
discusses a possible objection to this information-theoretic justification and
shows how it can be met. I then compare maximum entropy inference with certain
other currentl... | computer science |
19,327 | Independence and Bayesian Updating Methods | cs.AI | Duda, Hart, and Nilsson have set forth a method for rule-based inference
systems to use in updating the probabilities of hypotheses on the basis of
multiple items of new evidence. Pednault, Zucker, and Muresan claimed to give
conditions under which independence assumptions made by Duda et al. preclude
updating-that is,... | computer science |
19,328 | A Constraint Propagation Approach to Probabilistic Reasoning | cs.AI | The paper demonstrates that strict adherence to probability theory does not
preclude the use of concurrent, self-activated constraint-propagation
mechanisms for managing uncertainty. Maintaining local records of
sources-of-belief allows both predictive and diagnostic inferences to be
activated simultaneously and propag... | computer science |
19,329 | Relative Entropy, Probabilistic Inference and AI | cs.AI | Various properties of relative entropy have led to its widespread use in
information theory. These properties suggest that relative entropy has a role
to play in systems that attempt to perform inference in terms of probability
distributions. In this paper, I will review some basic properties of relative
entropy as wel... | computer science |
19,330 | Foundations of Probability Theory for AI - The Application of
Algorithmic Probability to Problems in Artificial Intelligence | cs.AI | This paper covers two topics: first an introduction to Algorithmic Complexity
Theory: how it defines probability, some of its characteristic properties and
past successful applications. Second, we apply it to problems in A.I. - where
it promises to give near optimum search procedures for two very broad classes
of probl... | computer science |
19,331 | Selecting Uncertainty Calculi and Granularity: An Experiment in
Trading-Off Precision and Complexity | cs.AI | The management of uncertainty in expert systems has usually been left to ad
hoc representations and rules of combinations lacking either a sound theory or
clear semantics. The objective of this paper is to establish a theoretical
basis for defining the syntax and semantics of a small subset of calculi of
uncertainty op... | computer science |
19,332 | A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions | cs.AI | Attempts to replicate probabilistic reasoning in expert systems have
typically overlooked a critical ingredient of that process. Probabilistic
analysis typically requires extensive judgments regarding interdependencies
among hypotheses and data, and regarding the appropriateness of various
alternative models. The appli... | computer science |
19,333 | Metaprobability and Dempster-Shafer in Evidential Reasoning | cs.AI | Evidential reasoning in expert systems has often used ad-hoc uncertainty
calculi. Although it is generally accepted that probability theory provides a
firm theoretical foundation, researchers have found some problems with its use
as a workable uncertainty calculus. Among these problems are representation of
ignorance, ... | computer science |
19,334 | Implementing Probabilistic Reasoning | cs.AI | General problems in analyzing information in a probabilistic database are
considered. The practical difficulties (and occasional advantages) of storing
uncertain data, of using it conventional forward- or backward-chaining
inference engines, and of working with a probabilistic version of resolution
are discussed. The b... | computer science |
19,335 | Probability Judgement in Artificial Intelligence | cs.AI | This paper is concerned with two theories of probability judgment: the
Bayesian theory and the theory of belief functions. It illustrates these
theories with some simple examples and discusses some of the issues that arise
when we try to implement them in expert systems. The Bayesian theory is well
known; its main idea... | computer science |
19,336 | A Framework for Comparing Uncertain Inference Systems to Probability | cs.AI | Several different uncertain inference systems (UISs) have been developed for
representing uncertainty in rule-based expert systems. Some of these, such as
Mycin's Certainty Factors, Prospector, and Bayes' Networks were designed as
approximations to probability, and others, such as Fuzzy Set Theory and
DempsterShafer Be... | computer science |
19,337 | Inductive Inference and the Representation of Uncertainty | cs.AI | The form and justification of inductive inference rules depend strongly on
the representation of uncertainty. This paper examines one generic
representation, namely, incomplete information. The notion can be formalized by
presuming that the relevant probabilities in a decision problem are known only
to the extent that ... | computer science |
19,338 | Induction, of and by Probability | cs.AI | This paper examines some methods and ideas underlying the author's successful
probabilistic learning systems(PLS), which have proven uniquely effective and
efficient in generalization learning or induction. While the emerging
principles are generally applicable, this paper illustrates them in heuristic
search, which de... | computer science |
19,339 | An Odds Ratio Based Inference Engine | cs.AI | Expert systems applications that involve uncertain inference can be
represented by a multidimensional contingency table. These tables offer a
general approach to inferring with uncertain evidence, because they can embody
any form of association between any number of pieces of evidence and
conclusions. (Simpler models m... | computer science |
19,340 | A Framework for Control Strategies in Uncertain Inference Networks | cs.AI | Control Strategies for hierarchical tree-like probabilistic inference
networks are formulated and investigated. Strategies that utilize staged
look-ahead and temporary focus on subgoals are formalized and refined using the
Depth Vector concept that serves as a tool for defining the 'virtual tree'
regarded by the contro... | computer science |
19,341 | Combining Uncertain Estimates | cs.AI | In a real expert system, one may have unreliable, unconfident, conflicting
estimates of the value for a particular parameter. It is important for decision
making that the information present in this aggregate somehow find its way into
use. We cast the problem of representing and combining uncertain estimates as
selecti... | computer science |
19,342 | Confidence Factors, Empiricism and the Dempster-Shafer Theory of
Evidence | cs.AI | The issue of confidence factors in Knowledge Based Systems has become
increasingly important and Dempster-Shafer (DS) theory has become increasingly
popular as a basis for these factors. This paper discusses the need for an
empirical lnterpretatlon of any theory of confidence factors applied to
Knowledge Based Systems ... | computer science |
19,343 | Incidence Calculus: A Mechanism for Probabilistic Reasoning | cs.AI | Mechanisms for the automation of uncertainty are required for expert systems.
Sometimes these mechanisms need to obey the properties of probabilistic
reasoning. A purely numeric mechanism, like those proposed so far, cannot
provide a probabilistic logic with truth functional connectives. We propose an
alternative mecha... | computer science |
19,344 | Evidential Confirmation as Transformed Probability | cs.AI | A considerable body of work in AI has been concerned with aggregating
measures of confirmatory and disconfirmatory evidence for a common set of
propositions. Claiming classical probability to be inadequate or inappropriate,
several researchers have gone so far as to invent new formalisms and methods.
We show how to rep... | computer science |
19,345 | Interval-Based Decisions for Reasoning Systems | cs.AI | This essay looks at decision-making with interval-valued probability
measures. Existing decision methods have either supplemented expected utility
methods with additional criteria of optimality, or have attempted to supplement
the interval-valued measures. We advocate a new approach, which makes the
following questions... | computer science |
19,346 | Machine Generalization and Human Categorization: An
Information-Theoretic View | cs.AI | In designing an intelligent system that must be able to explain its reasoning
to a human user, or to provide generalizations that the human user finds
reasonable, it may be useful to take into consideration psychological data on
what types of concepts and categories people naturally use. The psychological
literature on... | computer science |
19,347 | Exact Reasoning Under Uncertainty | cs.AI | This paper focuses on designing expert systems to support decision making in
complex, uncertain environments. In this context, our research indicates that
strictly probabilistic representations, which enable the use of
decision-theoretic reasoning, are highly preferable to recently proposed
alternatives (e.g., fuzzy se... | computer science |
19,348 | The Estimation of Subjective Probabilities via Categorical Judgments of
Uncertainty | cs.AI | Theoretically as well as experimentally it is investigated how people
represent their knowledge in order to make decisions or to share their
knowledge with others. Experiment 1 probes into the ways how people 6ather
information about the frequencies of events and how the requested response
mode, that is, numerical vs. ... | computer science |
19,349 | A Cure for Pathological Behavior in Games that Use Minimax | cs.AI | The traditional approach to choosing moves in game-playing programs is the
minimax procedure. The general belief underlying its use is that increasing
search depth improves play. Recent research has shown that given certain
simplifying assumptions about a game tree's structure, this belief is
erroneous: searching deepe... | computer science |
19,350 | An Evaluation of Two Alternatives to Minimax | cs.AI | In the field of Artificial Intelligence, traditional approaches to choosing
moves in games involve the we of the minimax algorithm. However, recent
research results indicate that minimizing may not always be the best approach.
In this paper we summarize the results of some measurements on several model
games with sever... | computer science |
19,351 | Intelligent Probabilistic Inference | cs.AI | The analysis of practical probabilistic models on the computer demands a
convenient representation for the available knowledge and an efficient
algorithm to perform inference. An appealing representation is the influence
diagram, a network that makes explicit the random variables in a model and
their probabilistic depe... | computer science |
19,352 | Strong & Weak Methods: A Logical View of Uncertainty | cs.AI | The last few years has seen a growing debate about techniques for managing
uncertainty in AI systems. Unfortunately this debate has been cast as a rivalry
between AI methods and classical probability based ones. Three arguments for
extending the probability framework of uncertainty are presented, none of which
imply a ... | computer science |
19,353 | Probabilistic Conflict Resolution in Hierarchical Hypothesis Spaces | cs.AI | Artificial intelligence applications such as industrial robotics, military
surveillance, and hazardous environment clean-up, require situation
understanding based on partial, uncertain, and ambiguous or erroneous evidence.
It is necessary to evaluate the relative likelihood of multiple possible
hypotheses of the (curre... | computer science |
19,354 | Knowledge Structures and Evidential Reasoning in Decision Analysis | cs.AI | The roles played by decision factors in making complex subject are decisions
are characterized by how these factors affect the overall decision. Evidence
that partially matches a factor is evaluated, and then effective computational
rules are applied to these roles to form an appropriate aggregation of the
evidence. Th... | computer science |
19,355 | Logical Stochastic Optimization | cs.AI | We present a logical framework to represent and reason about stochastic
optimization problems based on probability answer set programming. This is
established by allowing probability optimization aggregates, e.g., minimum and
maximum in the language of probability answer set programming to allow
minimization or maximiz... | computer science |
19,356 | Evolutionary Turing in the Context of Evolutionary Machines | cs.AI | One of the roots of evolutionary computation was the idea of Turing about
unorganized machines. The goal of this work is the development of foundations
for evolutionary computations, connecting Turing's ideas and the contemporary
state of art in evolutionary computations. To achieve this goal, we develop a
general appr... | computer science |
19,357 | Proceedings of the Sixteenth Conference on Uncertainty in Artificial
Intelligence (2000) | cs.AI | This is the Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence, which was held in San Francisco, CA, June 30 - July 3,
2000 | computer science |
19,358 | Proceedings of the Fifteenth Conference on Uncertainty in Artificial
Intelligence (1999) | cs.AI | This is the Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Stockholm Sweden, July 30 - August
1, 1999 | computer science |
19,359 | Proceedings of the Fourteenth Conference on Uncertainty in Artificial
Intelligence (1998) | cs.AI | This is the Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Madison, WI, July 24-26, 1998 | computer science |
19,360 | Proceedings of the Thirteenth Conference on Uncertainty in Artificial
Intelligence (1997) | cs.AI | This is the Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Providence, RI, August 1-3, 1997 | computer science |
19,361 | Proceedings of the Twelfth Conference on Uncertainty in Artificial
Intelligence (1996) | cs.AI | This is the Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence, which was held in Portland, OR, August 1-4, 1996 | computer science |
19,362 | Proceedings of the Eleventh Conference on Uncertainty in Artificial
Intelligence (1995) | cs.AI | This is the Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence, which was held in Montreal, QU, August 18-20, 1995 | computer science |
19,363 | Proceedings of the Tenth Conference on Uncertainty in Artificial
Intelligence (1994) | cs.AI | This is the Proceedings of the Tenth Conference on Uncertainty in Artificial
Intelligence, which was held in Seattle, WA, July 29-31, 1994 | computer science |
19,364 | Proceedings of the Ninth Conference on Uncertainty in Artificial
Intelligence (1993) | cs.AI | This is the Proceedings of the Ninth Conference on Uncertainty in Artificial
Intelligence, which was held in Washington, DC, July 9-11, 1993 | computer science |
19,365 | Proceedings of the Eighth Conference on Uncertainty in Artificial
Intelligence (1992) | cs.AI | This is the Proceedings of the Eighth Conference on Uncertainty in Artificial
Intelligence, which was held in Stanford, CA, July 17-19, 1992 | computer science |
19,366 | Proceedings of the Seventh Conference on Uncertainty in Artificial
Intelligence (1991) | cs.AI | This is the Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence, which was held in Los Angeles, CA, July 13-15, 1991 | computer science |
19,367 | Proceedings of the Sixth Conference on Uncertainty in Artificial
Intelligence (1990) | cs.AI | This is the Proceedings of the Sixth Conference on Uncertainty in Artificial
Intelligence, which was held in Cambridge, MA, Jul 27 - Jul 29, 1990 | computer science |
19,368 | Proceedings of the Fifth Conference on Uncertainty in Artificial
Intelligence (1989) | cs.AI | This is the Proceedings of the Fifth Conference on Uncertainty in Artificial
Intelligence, which was held in Windsor, ON, August 18-20, 1989 | computer science |
19,369 | Proceedings of the Fourth Conference on Uncertainty in Artificial
Intelligence (1988) | cs.AI | This is the Proceedings of the Fourth Conference on Uncertainty in Artificial
Intelligence, which was held in Minneapolis, MN, July 10-12, 1988 | computer science |
19,370 | Proceedings of the Third Conference on Uncertainty in Artificial
Intelligence (1987) | cs.AI | This is the Proceedings of the Third Conference on Uncertainty in Artificial
Intelligence, which was held in Seattle, WA, July 10-12, 1987 | computer science |
19,371 | Proceedings of the Second Conference on Uncertainty in Artificial
Intelligence (1986) | cs.AI | This is the Proceedings of the Second Conference on Uncertainty in Artificial
Intelligence, which was held in Philadelphia, PA, August 8-10, 1986 | computer science |
19,372 | Justificatory and Explanatory Argumentation for Committing Agents | cs.AI | In the interaction between agents we can have an explicative discourse, when
communicating preferences or intentions, and a normative discourse, when
considering normative knowledge. For justifying their actions our agents are
endowed with a Justification and Explanation Logic (JEL), capable to cover both
the justifica... | computer science |
19,373 | Proceedings of the First Conference on Uncertainty in Artificial
Intelligence (1985) | cs.AI | This is the Proceedings of the First Conference on Uncertainty in Artificial
Intelligence, which was held in Los Angeles, CA, July 10-12, 1985 | computer science |
19,374 | RockIt: Exploiting Parallelism and Symmetry for MAP Inference in
Statistical Relational Models | cs.AI | RockIt is a maximum a-posteriori (MAP) query engine for statistical
relational models. MAP inference in graphical models is an optimization problem
which can be compiled to integer linear programs (ILPs). We describe several
advances in translating MAP queries to ILP instances and present the novel
meta-algorithm cutti... | computer science |
19,375 | Mining to Compact CNF Propositional Formulae | cs.AI | In this paper, we propose a first application of data mining techniques to
propositional satisfiability. Our proposed Mining4SAT approach aims to discover
and to exploit hidden structural knowledge for reducing the size of
propositional formulae in conjunctive normal form (CNF). Mining4SAT combines
both frequent itemse... | computer science |
19,376 | h-approximation: History-Based Approximation of Possible World Semantics
as ASP | cs.AI | We propose an approximation of the Possible Worlds Semantics (PWS) for action
planning. A corresponding planning system is implemented by a transformation of
the action specification to an Answer-Set Program. A novelty is support for
postdiction wrt. (a) the plan existence problem in our framework can be solved
in NP, ... | computer science |
19,377 | Improvement/Extension of Modular Systems as Combinatorial Reengineering
(Survey) | cs.AI | The paper describes development (improvement/extension) approaches for
composite (modular) systems (as combinatorial reengineering). The following
system improvement/extension actions are considered: (a) improvement of systems
component(s) (e.g., improvement of a system component, replacement of a system
component); (b... | computer science |
19,378 | Solving WCSP by Extraction of Minimal Unsatisfiable Cores | cs.AI | Usual techniques to solve WCSP are based on cost transfer operations coupled
with a branch and bound algorithm. In this paper, we focus on an approach
integrating extraction and relaxation of Minimal Unsatisfiable Cores in order
to solve this problem. We decline our approach in two ways: an incomplete,
greedy, algorith... | computer science |
19,379 | OntoRich - A Support Tool for Semi-Automatic Ontology Enrichment and
Evaluation | cs.AI | This paper presents the OntoRich framework, a support tool for semi-automatic
ontology enrichment and evaluation. The WordNet is used to extract candidates
for dynamic ontology enrichment from RSS streams. With the integration of
OpenNLP the system gains access to syntactic analysis of the RSS news. The
enriched ontolo... | computer science |
19,380 | Enacting Social Argumentative Machines in Semantic Wikipedia | cs.AI | This research advocates the idea of combining argumentation theory with the
social web technology, aiming to enact large scale or mass argumentation. The
proposed framework allows mass-collaborative editing of structured arguments in
the style of semantic wikipedia. The long term goal is to apply the abstract
machinery... | computer science |
19,381 | A novice looks at emotional cognition | cs.AI | Modeling emotional-cognition is in a nascent stage and therefore wide-open
for new ideas and discussions. In this paper the author looks at the modeling
problem by bringing in ideas from axiomatic mathematics, information theory,
computer science, molecular biology, non-linear dynamical systems and quantum
computing an... | computer science |
19,382 | Exchanging OWL 2 QL Knowledge Bases | cs.AI | Knowledge base exchange is an important problem in the area of data exchange
and knowledge representation, where one is interested in exchanging information
between a source and a target knowledge base connected through a mapping. In
this paper, we study this fundamental problem for knowledge bases and mappings
express... | computer science |
19,383 | Towards an Extension of the 2-tuple Linguistic Model to Deal With
Unbalanced Linguistic Term sets | cs.AI | In the domain of Computing with words (CW), fuzzy linguistic approaches are
known to be relevant in many decision-making problems. Indeed, they allow us to
model the human reasoning in replacing words, assessments, preferences,
choices, wishes... by ad hoc variables, such as fuzzy sets or more
sophisticated variables.
... | computer science |
19,384 | Three Generalizations of the FOCUS Constraint | cs.AI | The FOCUS constraint expresses the notion that solutions are concentrated. In
practice, this constraint suffers from the rigidity of its semantics. To tackle
this issue, we propose three generalizations of the FOCUS constraint. We
provide for each one a complete filtering algorithm as well as discussing
decompositions. | computer science |
19,385 | Automating the Dispute Resolution in Task Dependency Network | cs.AI | When perturbation or unexpected events do occur, agents need protocols for
repairing or reforming the supply chain. Unfortunate contingency could increase
too much the cost of performance, while breaching the current contract may be
more efficient. In our framework the principles of contract law are applied to
set pena... | computer science |
19,386 | Verification of Inconsistency-Aware Knowledge and Action Bases (Extended
Version) | cs.AI | Description Logic Knowledge and Action Bases (KABs) have been recently
introduced as a mechanism that provides a semantically rich representation of
the information on the domain of interest in terms of a DL KB and a set of
actions to change such information over time, possibly introducing new objects.
In this setting,... | computer science |
19,387 | Non Deterministic Logic Programs | cs.AI | Non deterministic applications arise in many domains, including, stochastic
optimization, multi-objectives optimization, stochastic planning, contingent
stochastic planning, reinforcement learning, reinforcement learning in
partially observable Markov decision processes, and conditional planning. We
present a logic pro... | computer science |
19,388 | Solution of the Decision Making Problems using Fuzzy Soft Relations | cs.AI | The Fuzzy Modeling has been applied in a wide variety of fields such as
Engineering and Management Sciences and Social Sciences to solve a number
Decision Making Problems which involve impreciseness, uncertainty and vagueness
in data. In particular, applications of this Modeling technique in Decision
Making Problems ha... | computer science |
19,389 | Solution of System of Linear Equations - A Neuro-Fuzzy Approach | cs.AI | Neuro-Fuzzy Modeling has been applied in a wide variety of fields such as
Decision Making, Engineering and Management Sciences etc. In particular,
applications of this Modeling technique in Decision Making by involving complex
Systems of Linear Algebraic Equations have remarkable significance. In this
Paper, we present... | computer science |
19,390 | Universal Induction with Varying Sets of Combinators | cs.AI | Universal induction is a crucial issue in AGI. Its practical applicability
can be achieved by the choice of the reference machine or representation of
algorithms agreed with the environment. This machine should be updatable for
solving subsequent tasks more efficiently. We study this problem on an example
of combinator... | computer science |
19,391 | First-Order Decomposition Trees | cs.AI | Lifting attempts to speed up probabilistic inference by exploiting symmetries
in the model. Exact lifted inference methods, like their propositional
counterparts, work by recursively decomposing the model and the problem. In the
propositional case, there exist formal structures, such as decomposition trees
(dtrees), th... | computer science |
19,392 | LLAMA: Leveraging Learning to Automatically Manage Algorithms | cs.AI | Algorithm portfolio and selection approaches have achieved remarkable
improvements over single solvers. However, the implementation of such systems
is often highly customised and specific to the problem domain. This makes it
difficult for researchers to explore different techniques for their specific
problems. We prese... | computer science |
19,393 | Direct Uncertainty Estimation in Reinforcement Learning | cs.AI | Optimal probabilistic approach in reinforcement learning is computationally
infeasible. Its simplification consisting in neglecting difference between true
environment and its model estimated using limited number of observations causes
exploration vs exploitation problem. Uncertainty can be expressed in terms of a
prob... | computer science |
19,394 | Extending Universal Intelligence Models with Formal Notion of
Representation | cs.AI | Solomonoff induction is known to be universal, but incomputable. Its
approximations, namely, the Minimum Description (or Message) Length (MDL)
principles, are adopted in practice in the efficient, but non-universal form.
Recent attempts to bridge this gap leaded to development of the
Representational MDL principle that... | computer science |
19,395 | Flexibly-bounded Rationality and Marginalization of Irrationality
Theories for Decision Making | cs.AI | In this paper the theory of flexibly-bounded rationality which is an
extension to the theory of bounded rationality is revisited. Rational decision
making involves using information which is almost always imperfect and
incomplete together with some intelligent machine which if it is a human being
is inconsistent to mak... | computer science |
19,396 | The Effect of Biased Communications On Both Trusting and Suspicious
Voters | cs.AI | In recent studies of political decision-making, apparently anomalous behavior
has been observed on the part of voters, in which negative information about a
candidate strengthens, rather than weakens, a prior positive opinion about the
candidate. This behavior appears to run counter to rational models of decision
makin... | computer science |
19,397 | Sparse Auto-Regressive: Robust Estimation of AR Parameters | cs.AI | In this paper I present a new approach for regression of time series using
their own samples. This is a celebrated problem known as Auto-Regression.
Dealing with outlier or missed samples in a time series makes the problem of
estimation difficult, so it should be robust against them. Moreover for coding
purposes I will... | computer science |
19,398 | Encoding Petri Nets in Answer Set Programming for Simulation Based
Reasoning | cs.AI | One of our long term research goals is to develop systems to answer realistic
questions (e.g., some mentioned in textbooks) about biological pathways that a
biologist may ask. To answer such questions we need formalisms that can model
pathways, simulate their execution, model intervention to those pathways, and
compare... | computer science |
19,399 | Encoding Higher Level Extensions of Petri Nets in Answer Set Programming | cs.AI | Answering realistic questions about biological systems and pathways similar
to the ones used by text books to test understanding of students about
biological systems is one of our long term research goals. Often these
questions require simulation based reasoning. To answer such questions, we need
formalisms to build pa... | computer science |
19,400 | The Rise and Fall of Semantic Rule Updates Based on SE-Models | cs.AI | Logic programs under the stable model semantics, or answer-set programs,
provide an expressive rule-based knowledge representation framework, featuring
a formal, declarative and well-understood semantics. However, handling the
evolution of rule bases is still a largely open problem. The AGM framework for
belief change ... | computer science |
19,401 | The SP theory of intelligence: an overview | cs.AI | This article is an overview of the "SP theory of intelligence". The theory
aims to simplify and integrate concepts across artificial intelligence,
mainstream computing and human perception and cognition, with information
compression as a unifying theme. It is conceived as a brain-like system that
receives 'New' informa... | computer science |
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