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17,902 | Diffusion of Context and Credit Information in Markovian Models | cs.AI | This paper studies the problem of ergodicity of transition probability
matrices in Markovian models, such as hidden Markov models (HMMs), and how it
makes very difficult the task of learning to represent long-term context for
sequential data. This phenomenon hurts the forward propagation of long-term
context informatio... | computer science |
17,903 | Improving Connectionist Energy Minimization | cs.AI | Symmetric networks designed for energy minimization such as Boltzman machines
and Hopfield nets are frequently investigated for use in optimization,
constraint satisfaction and approximation of NP-hard problems. Nevertheless,
finding a global solution (i.e., a global minimum for the energy function) is
not guaranteed a... | computer science |
17,904 | Learning Membership Functions in a Function-Based Object Recognition
System | cs.AI | Functionality-based recognition systems recognize objects at the category
level by reasoning about how well the objects support the expected function.
Such systems naturally associate a ``measure of goodness'' or ``membership
value'' with a recognized object. This measure of goodness is the result of
combining individu... | computer science |
17,905 | Flexibly Instructable Agents | cs.AI | This paper presents an approach to learning from situated, interactive
tutorial instruction within an ongoing agent. Tutorial instruction is a
flexible (and thus powerful) paradigm for teaching tasks because it allows an
instructor to communicate whatever types of knowledge an agent might need in
whatever situations mi... | computer science |
17,906 | OPUS: An Efficient Admissible Algorithm for Unordered Search | cs.AI | OPUS is a branch and bound search algorithm that enables efficient admissible
search through spaces for which the order of search operator application is not
significant. The algorithm's search efficiency is demonstrated with respect to
very large machine learning search spaces. The use of admissible search is of
poten... | computer science |
17,907 | Vision-Based Road Detection in Automotive Systems: A Real-Time
Expectation-Driven Approach | cs.AI | The main aim of this work is the development of a vision-based road detection
system fast enough to cope with the difficult real-time constraints imposed by
moving vehicle applications. The hardware platform, a special-purpose massively
parallel system, has been chosen to minimize system production and operational
cost... | computer science |
17,908 | Generalization of Clauses under Implication | cs.AI | In the area of inductive learning, generalization is a main operation, and
the usual definition of induction is based on logical implication. Recently
there has been a rising interest in clausal representation of knowledge in
machine learning. Almost all inductive learning systems that perform
generalization of clauses... | computer science |
17,909 | Decision-Theoretic Foundations for Causal Reasoning | cs.AI | We present a definition of cause and effect in terms of decision-theoretic
primitives and thereby provide a principled foundation for causal reasoning.
Our definition departs from the traditional view of causation in that causal
assertions may vary with the set of decisions available. We argue that this
approach provid... | computer science |
17,910 | Translating between Horn Representations and their Characteristic Models | cs.AI | Characteristic models are an alternative, model based, representation for
Horn expressions. It has been shown that these two representations are
incomparable and each has its advantages over the other. It is therefore
natural to ask what is the cost of translating, back and forth, between these
representations. Interes... | computer science |
17,911 | Statistical Feature Combination for the Evaluation of Game Positions | cs.AI | This article describes an application of three well-known statistical methods
in the field of game-tree search: using a large number of classified Othello
positions, feature weights for evaluation functions with a
game-phase-independent meaning are estimated by means of logistic regression,
Fisher's linear discriminant... | computer science |
17,912 | Rule-based Machine Learning Methods for Functional Prediction | cs.AI | We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction of ... | computer science |
17,913 | The Design and Experimental Analysis of Algorithms for Temporal
Reasoning | cs.AI | Many applications -- from planning and scheduling to problems in molecular
biology -- rely heavily on a temporal reasoning component. In this paper, we
discuss the design and empirical analysis of algorithms for a temporal
reasoning system based on Allen's influential interval-based framework for
representing temporal ... | computer science |
17,914 | Well-Founded Semantics for Extended Logic Programs with Dynamic
Preferences | cs.AI | The paper describes an extension of well-founded semantics for logic programs
with two types of negation. In this extension information about preferences
between rules can be expressed in the logical language and derived dynamically.
This is achieved by using a reserved predicate symbol and a naming technique.
Conflict... | computer science |
17,915 | Logarithmic-Time Updates and Queries in Probabilistic Networks | cs.AI | Traditional databases commonly support efficient query and update procedures
that operate in time which is sublinear in the size of the database. Our goal
in this paper is to take a first step toward dynamic reasoning in probabilistic
databases with comparable efficiency. We propose a dynamic data structure that
suppor... | computer science |
17,916 | Quantum Computing and Phase Transitions in Combinatorial Search | cs.AI | We introduce an algorithm for combinatorial search on quantum computers that
is capable of significantly concentrating amplitude into solutions for some NP
search problems, on average. This is done by exploiting the same aspects of
problem structure as used by classical backtrack methods to avoid unproductive
search ch... | computer science |
17,917 | Mean Field Theory for Sigmoid Belief Networks | cs.AI | We develop a mean field theory for sigmoid belief networks based on ideas
from statistical mechanics. Our mean field theory provides a tractable
approximation to the true probability distribution in these networks; it also
yields a lower bound on the likelihood of evidence. We demonstrate the utility
of this framework ... | computer science |
17,918 | Improved Use of Continuous Attributes in C4.5 | cs.AI | A reported weakness of C4.5 in domains with continuous attributes is
addressed by modifying the formation and evaluation of tests on continuous
attributes. An MDL-inspired penalty is applied to such tests, eliminating some
of them from consideration and altering the relative desirability of all tests.
Empirical trials ... | computer science |
17,919 | Active Learning with Statistical Models | cs.AI | For many types of machine learning algorithms, one can compute the
statistically `optimal' way to select training data. In this paper, we review
how optimal data selection techniques have been used with feedforward neural
networks. We then show how the same principles may be used to select data for
two alternative, sta... | computer science |
17,920 | A Divergence Critic for Inductive Proof | cs.AI | Inductive theorem provers often diverge. This paper describes a simple
critic, a computer program which monitors the construction of inductive proofs
attempting to identify diverging proof attempts. Divergence is recognized by
means of a ``difference matching'' procedure. The critic then proposes lemmas
and generalizat... | computer science |
17,921 | Practical Methods for Proving Termination of General Logic Programs | cs.AI | Termination of logic programs with negated body atoms (here called general
logic programs) is an important topic. One reason is that many computational
mechanisms used to process negated atoms, like Clark's negation as failure and
Chan's constructive negation, are based on termination conditions. This paper
introduces ... | computer science |
17,922 | Iterative Optimization and Simplification of Hierarchical Clusterings | cs.AI | Clustering is often used for discovering structure in data. Clustering
systems differ in the objective function used to evaluate clustering quality
and the control strategy used to search the space of clusterings. Ideally, the
search strategy should consistently construct clusterings of high quality, but
be computation... | computer science |
17,923 | Further Experimental Evidence against the Utility of Occam's Razor | cs.AI | This paper presents new experimental evidence against the utility of Occam's
razor. A~systematic procedure is presented for post-processing decision trees
produced by C4.5. This procedure was derived by rejecting Occam's razor and
instead attending to the assumption that similar objects are likely to belong
to the same... | computer science |
17,924 | Least Generalizations and Greatest Specializations of Sets of Clauses | cs.AI | The main operations in Inductive Logic Programming (ILP) are generalization
and specialization, which only make sense in a generality order. In ILP, the
three most important generality orders are subsumption, implication and
implication relative to background knowledge. The two languages used most often
are languages o... | computer science |
17,925 | Reinforcement Learning: A Survey | cs.AI | This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent t... | computer science |
17,926 | Adaptive Problem-solving for Large-scale Scheduling Problems: A Case
Study | cs.AI | Although most scheduling problems are NP-hard, domain specific techniques
perform well in practice but are quite expensive to construct. In adaptive
problem-solving solving, domain specific knowledge is acquired automatically
for a general problem solver with a flexible control architecture. In this
approach, a learnin... | computer science |
17,927 | A Formal Framework for Speedup Learning from Problems and Solutions | cs.AI | Speedup learning seeks to improve the computational efficiency of problem
solving with experience. In this paper, we develop a formal framework for
learning efficient problem solving from random problems and their solutions. We
apply this framework to two different representations of learned knowledge,
namely control r... | computer science |
17,928 | 2Planning for Contingencies: A Decision-based Approach | cs.AI | A fundamental assumption made by classical AI planners is that there is no
uncertainty in the world: the planner has full knowledge of the conditions
under which the plan will be executed and the outcome of every action is fully
predictable. These planners cannot therefore construct contingency plans, i.e.,
plans in wh... | computer science |
17,929 | A Principled Approach Towards Symbolic Geometric Constraint Satisfaction | cs.AI | An important problem in geometric reasoning is to find the configuration of a
collection of geometric bodies so as to satisfy a set of given constraints.
Recently, it has been suggested that this problem can be solved efficiently by
symbolically reasoning about geometry. This approach, called degrees of freedom
analysi... | computer science |
17,930 | On Partially Controlled Multi-Agent Systems | cs.AI | Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct control... | computer science |
17,931 | Spatial Aggregation: Theory and Applications | cs.AI | Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computati... | computer science |
17,932 | A Hierarchy of Tractable Subsets for Computing Stable Models | cs.AI | Finding the stable models of a knowledge base is a significant computational
problem in artificial intelligence. This task is at the computational heart of
truth maintenance systems, autoepistemic logic, and default logic.
Unfortunately, it is NP-hard. In this paper we present a hierarchy of classes
of knowledge bases,... | computer science |
17,933 | Accelerating Partial-Order Planners: Some Techniques for Effective
Search Control and Pruning | cs.AI | We propose some domain-independent techniques for bringing well-founded
partial-order planners closer to practicality. The first two techniques are
aimed at improving search control while keeping overhead costs low. One is
based on a simple adjustment to the default A* heuristic used by UCPOP to
select plans for refine... | computer science |
17,934 | Cue Phrase Classification Using Machine Learning | cs.AI | Cue phrases may be used in a discourse sense to explicitly signal discourse
structure, but also in a sentential sense to convey semantic rather than
structural information. Correctly classifying cue phrases as discourse or
sentential is critical in natural language processing systems that exploit
discourse structure, e... | computer science |
17,935 | Mechanisms for Automated Negotiation in State Oriented Domains | cs.AI | This paper lays part of the groundwork for a domain theory of negotiation,
that is, a way of classifying interactions so that it is clear, given a domain,
which negotiation mechanisms and strategies are appropriate. We define State
Oriented Domains, a general category of interaction. Necessary and sufficient
conditions... | computer science |
17,936 | Learning First-Order Definitions of Functions | cs.AI | First-order learning involves finding a clause-form definition of a relation
from examples of the relation and relevant background information. In this
paper, a particular first-order learning system is modified to customize it for
finding definitions of functional relations. This restriction leads to faster
learning t... | computer science |
17,937 | MUSE CSP: An Extension to the Constraint Satisfaction Problem | cs.AI | This paper describes an extension to the constraint satisfaction problem
(CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem).
This extension is especially useful for those problems which segment into
multiple sets of partially shared variables. Such problems arise naturally in
signal processing a... | computer science |
17,938 | Exploiting Causal Independence in Bayesian Network Inference | cs.AI | A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as representing a
factorization of a joint probability into the multiplication of a set of
conditional probabilities. We present a notion of causal independence that
enables one to further... | computer science |
17,939 | Quantitative Results Comparing Three Intelligent Interfaces for
Information Capture: A Case Study Adding Name Information into an Electronic
Personal Organizer | cs.AI | Efficiently entering information into a computer is key to enjoying the
benefits of computing. This paper describes three intelligent user interfaces:
handwriting recognition, adaptive menus, and predictive fillin. In the context
of adding a personUs name and address to an electronic organizer, tests show
handwriting r... | computer science |
17,940 | Characterizations of Decomposable Dependency Models | cs.AI | Decomposable dependency models possess a number of interesting and useful
properties. This paper presents new characterizations of decomposable models in
terms of independence relationships, which are obtained by adding a single
axiom to the well-known set characterizing dependency models that are
isomorphic to undirec... | computer science |
17,941 | Improved Heterogeneous Distance Functions | cs.AI | Instance-based learning techniques typically handle continuous and linear
input values well, but often do not handle nominal input attributes
appropriately. The Value Difference Metric (VDM) was designed to find
reasonable distance values between nominal attribute values, but it largely
ignores continuous attributes, r... | computer science |
17,942 | SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis
Using Artificial Neural Networks | cs.AI | Previous approaches of analyzing spontaneously spoken language often have
been based on encoding syntactic and semantic knowledge manually and
symbolically. While there has been some progress using statistical or
connectionist language models, many current spoken- language systems still use
a relatively brittle, hand-c... | computer science |
17,943 | A Uniform Framework for Concept Definitions in Description Logics | cs.AI | Most modern formalisms used in Databases and Artificial Intelligence for
describing an application domain are based on the notions of class (or concept)
and relationship among classes. One interesting feature of such formalisms is
the possibility of defining a class, i.e., providing a set of properties that
precisely c... | computer science |
17,944 | Lifeworld Analysis | cs.AI | We argue that the analysis of agent/environment interactions should be
extended to include the conventions and invariants maintained by agents
throughout their activity. We refer to this thicker notion of environment as a
lifeworld and present a partial set of formal tools for describing structures
of lifeworlds and th... | computer science |
17,945 | Query DAGs: A Practical Paradigm for Implementing Belief-Network
Inference | cs.AI | We describe a new paradigm for implementing inference in belief networks,
which consists of two steps: (1) compiling a belief network into an arithmetic
expression called a Query DAG (Q-DAG); and (2) answering queries using a simple
evaluation algorithm. Each node of a Q-DAG represents a numeric operation, a
number, or... | computer science |
17,946 | Connectionist Theory Refinement: Genetically Searching the Space of
Network Topologies | cs.AI | An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology ... | computer science |
17,947 | Flaw Selection Strategies for Partial-Order Planning | cs.AI | Several recent studies have compared the relative efficiency of alternative
flaw selection strategies for partial-order causal link (POCL) planning. We
review this literature, and present new experimental results that generalize
the earlier work and explain some of the discrepancies in it. In particular, we
describe th... | computer science |
17,948 | A Complete Classification of Tractability in RCC-5 | cs.AI | We investigate the computational properties of the spatial algebra RCC-5
which is a restricted version of the RCC framework for spatial reasoning. The
satisfiability problem for RCC-5 is known to be NP-complete but not much is
known about its approximately four billion subclasses. We provide a complete
classification o... | computer science |
17,949 | A New Look at the Easy-Hard-Easy Pattern of Combinatorial Search
Difficulty | cs.AI | The easy-hard-easy pattern in the difficulty of combinatorial search problems
as constraints are added has been explained as due to a competition between the
decrease in number of solutions and increased pruning. We test the generality
of this explanation by examining one of its predictions: if the number of
solutions ... | computer science |
17,950 | Eight Maximal Tractable Subclasses of Allen's Algebra with Metric Time | cs.AI | This paper combines two important directions of research in temporal
resoning: that of finding maximal tractable subclasses of Allen's interval
algebra, and that of reasoning with metric temporal information. Eight new
maximal tractable subclasses of Allen's interval algebra are presented, some of
them subsuming previo... | computer science |
17,951 | Defining Relative Likelihood in Partially-Ordered Preferential
Structures | cs.AI | Starting with a likelihood or preference order on worlds, we extend it to a
likelihood ordering on sets of worlds in a natural way, and examine the
resulting logic. Lewis earlier considered such a notion of relative likelihood
in the context of studying counterfactuals, but he assumed a total preference
order on worlds... | computer science |
17,952 | Towards Flexible Teamwork | cs.AI | Many AI researchers are today striving to build agent teams for complex,
dynamic multi-agent domains, with intended applications in arenas such as
education, training, entertainment, information integration, and collective
robotics. Unfortunately, uncertainties in these complex, dynamic domains
obstruct coherent teamwo... | computer science |
17,953 | Identifying Hierarchical Structure in Sequences: A linear-time algorithm | cs.AI | SEQUITUR is an algorithm that infers a hierarchical structure from a sequence
of discrete symbols by replacing repeated phrases with a grammatical rule that
generates the phrase, and continuing this process recursively. The result is a
hierarchical representation of the original sequence, which offers insights
into its... | computer science |
17,954 | Storing and Indexing Plan Derivations through Explanation-based Analysis
of Retrieval Failures | cs.AI | Case-Based Planning (CBP) provides a way of scaling up domain-independent
planning to solve large problems in complex domains. It replaces the detailed
and lengthy search for a solution with the retrieval and adaptation of previous
planning experiences. In general, CBP has been demonstrated to improve
performance over ... | computer science |
17,955 | A Model Approximation Scheme for Planning in Partially Observable
Stochastic Domains | cs.AI | Partially observable Markov decision processes (POMDPs) are a natural model
for planning problems where effects of actions are nondeterministic and the
state of the world is not completely observable. It is difficult to solve
POMDPs exactly. This paper proposes a new approximation scheme. The basic idea
is to transform... | computer science |
17,956 | Dynamic Non-Bayesian Decision Making | cs.AI | The model of a non-Bayesian agent who faces a repeated game with incomplete
information against Nature is an appropriate tool for modeling general
agent-environment interactions. In such a model the environment state
(controlled by Nature) may change arbitrarily, and the feedback/reward function
is initially unknown. T... | computer science |
17,957 | When Gravity Fails: Local Search Topology | cs.AI | Local search algorithms for combinatorial search problems frequently
encounter a sequence of states in which it is impossible to improve the value
of the objective function; moves through these regions, called plateau moves,
dominate the time spent in local search. We analyze and characterize plateaus
for three differe... | computer science |
17,958 | Bidirectional Heuristic Search Reconsidered | cs.AI | The assessment of bidirectional heuristic search has been incorrect since it
was first published more than a quarter of a century ago. For quite a long
time, this search strategy did not achieve the expected results, and there was
a major misunderstanding about the reasons behind it. Although there is still
wide-spread... | computer science |
17,959 | Incremental Recompilation of Knowledge | cs.AI | Approximating a general formula from above and below by Horn formulas (its
Horn envelope and Horn core, respectively) was proposed by Selman and Kautz
(1991, 1996) as a form of ``knowledge compilation,'' supporting rapid
approximate reasoning; on the negative side, this scheme is static in that it
supports no updates, ... | computer science |
17,960 | Monotonicity and Persistence in Preferential Logics | cs.AI | An important characteristic of many logics for Artificial Intelligence is
their nonmonotonicity. This means that adding a formula to the premises can
invalidate some of the consequences. There may, however, exist formulae that
can always be safely added to the premises without destroying any of the
consequences: we say... | computer science |
17,961 | Synthesizing Customized Planners from Specifications | cs.AI | Existing plan synthesis approaches in artificial intelligence fall into two
categories -- domain independent and domain dependent. The domain independent
approaches are applicable across a variety of domains, but may not be very
efficient in any one given domain. The domain dependent approaches need to be
(re)designed ... | computer science |
17,962 | Cached Sufficient Statistics for Efficient Machine Learning with Large
Datasets | cs.AI | This paper introduces new algorithms and data structures for quick counting
for machine learning datasets. We focus on the counting task of constructing
contingency tables, but our approach is also applicable to counting the number
of records in a dataset that match conjunctive queries. Subject to certain
assumptions, ... | computer science |
17,963 | Tractability of Theory Patching | cs.AI | In this paper we consider the problem of `theory patching', in which we are
given a domain theory, some of whose components are indicated to be possibly
flawed, and a set of labeled training examples for the domain concept. The
theory patching problem is to revise only the indicated components of the
theory, such that ... | computer science |
17,964 | Integrative Windowing | cs.AI | In this paper we re-investigate windowing for rule learning algorithms. We
show that, contrary to previous results for decision tree learning, windowing
can in fact achieve significant run-time gains in noise-free domains and
explain the different behavior of rule learning algorithms by the fact that
they learn each ru... | computer science |
17,965 | Model-Based Diagnosis using Structured System Descriptions | cs.AI | This paper presents a comprehensive approach for model-based diagnosis which
includes proposals for characterizing and computing preferred diagnoses,
assuming that the system description is augmented with a system structure (a
directed graph explicating the interconnections between system components).
Specifically, we ... | computer science |
17,966 | A Selective Macro-learning Algorithm and its Application to the NxN
Sliding-Tile Puzzle | cs.AI | One of the most common mechanisms used for speeding up problem solvers is
macro-learning. Macros are sequences of basic operators acquired during problem
solving. Macros are used by the problem solver as if they were basic operators.
The major problem that macro-learning presents is the vast number of macros
that are a... | computer science |
17,967 | The Computational Complexity of Probabilistic Planning | cs.AI | We examine the computational complexity of testing and finding small plans in
probabilistic planning domains with both flat and propositional
representations. The complexity of plan evaluation and existence varies with
the plan type sought; we examine totally ordered plans, acyclic plans, and
looping plans, and partial... | computer science |
17,968 | SYNERGY: A Linear Planner Based on Genetic Programming | cs.AI | In this paper we describe SYNERGY, which is a highly parallelizable, linear
planning system that is based on the genetic programming paradigm. Rather than
reasoning about the world it is planning for, SYNERGY uses artificial
selection, recombination and fitness measure to generate linear plans that
solve conjunctive go... | computer science |
17,969 | The Essence of Constraint Propagation | cs.AI | We show that several constraint propagation algorithms (also called (local)
consistency, consistency enforcing, Waltz, filtering or narrowing algorithms)
are instances of algorithms that deal with chaotic iteration. To this end we
propose a simple abstract framework that allows us to classify and compare
these algorith... | computer science |
17,970 | Towards a computational theory of human daydreaming | cs.AI | This paper examines the phenomenon of daydreaming: spontaneously recalling or
imagining personal or vicarious experiences in the past or future. The
following important roles of daydreaming in human cognition are postulated:
plan preparation and rehearsal, learning from failures and successes, support
for processes of ... | computer science |
17,971 | A reusable iterative optimization software library to solve
combinatorial problems with approximate reasoning | cs.AI | Real world combinatorial optimization problems such as scheduling are
typically too complex to solve with exact methods. Additionally, the problems
often have to observe vaguely specified constraints of different importance,
the available data may be uncertain, and compromises between antagonistic
criteria may be neces... | computer science |
17,972 | Modeling Belief in Dynamic Systems, Part II: Revision and Update | cs.AI | The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. In a companion paper (Friedman & Halpern,
1997), we introduce a new framework to model belief change. This framework
combines tempo... | computer science |
17,973 | The Symbol Grounding Problem | cs.AI | How can the semantic interpretation of a formal symbol system be made
intrinsic to the system, rather than just parasitic on the meanings in our
heads? How can the meanings of the meaningless symbol tokens, manipulated
solely on the basis of their (arbitrary) shapes, be grounded in anything but
other meaningless symbol... | computer science |
17,974 | Iterative Deepening Branch and Bound | cs.AI | In tree search problem the best-first search algorithm needs too much of
space . To remove such drawbacks of these algorithms the IDA* was developed
which is both space and time cost efficient. But again IDA* can give an optimal
solution for real valued problems like Flow shop scheduling, Travelling
Salesman and 0/1 Kn... | computer science |
17,975 | Probabilistic Agent Programs | cs.AI | Agents are small programs that autonomously take actions based on changes in
their environment or ``state.'' Over the last few years, there have been an
increasing number of efforts to build agents that can interact and/or
collaborate with other agents. In one of these efforts, Eiter, Subrahmanian amd
Pick (AIJ, 108(1-... | computer science |
17,976 | Cox's Theorem Revisited | cs.AI | The assumptions needed to prove Cox's Theorem are discussed and examined.
Various sets of assumptions under which a Cox-style theorem can be proved are
provided, although all are rather strong and, arguably, not natural. | computer science |
17,977 | Uniform semantic treatment of default and autoepistemic logics | cs.AI | We revisit the issue of connections between two leading formalisms in
nonmonotonic reasoning: autoepistemic logic and default logic. For each logic
we develop a comprehensive semantic framework based on the notion of a belief
pair. The set of all belief pairs together with the so called knowledge
ordering forms a compl... | computer science |
17,978 | On the accuracy and running time of GSAT | cs.AI | Randomized algorithms for deciding satisfiability were shown to be effective
in solving problems with thousands of variables. However, these algorithms are
not complete. That is, they provide no guarantee that a satisfying assignment,
if one exists, will be found. Thus, when studying randomized algorithms, there
are tw... | computer science |
17,979 | Syntactic Autonomy: Why There is no Autonomy without Symbols and How
Self-Organization Might Evolve Them | cs.AI | Two different types of agency are discussed based on dynamically coherent and
incoherent couplings with an environment respectively. I propose that until a
private syntax (syntactic autonomy) is discovered by dynamically coherent
agents, there are no significant or interesting types of closure or autonomy.
When syntact... | computer science |
17,980 | Consistency Management of Normal Logic Program by Top-down Abductive
Proof Procedure | cs.AI | This paper presents a method of computing a revision of a function-free
normal logic program. If an added rule is inconsistent with a program, that is,
if it leads to a situation such that no stable model exists for a new program,
then deletion and addition of rules are performed to avoid inconsistency. We
specify a re... | computer science |
17,981 | Defeasible Reasoning in OSCAR | cs.AI | This is a system description for the OSCAR defeasible reasoner. | computer science |
17,982 | Abductive and Consistency-Based Diagnosis Revisited: a Modeling
Perspective | cs.AI | Diagnostic reasoning has been characterized logically as consistency-based
reasoning or abductive reasoning. Previous analyses in the literature have
shown, on the one hand, that choosing the (in general more restrictive)
abductive definition may be appropriate or not, depending on the content of the
knowledge base [Co... | computer science |
17,983 | ACLP: Integrating Abduction and Constraint Solving | cs.AI | ACLP is a system which combines abductive reasoning and constraint solving by
integrating the frameworks of Abductive Logic Programming (ALP) and Constraint
Logic Programming (CLP). It forms a general high-level knowledge representation
environment for abductive problems in Artificial Intelligence and other areas.
In A... | computer science |
17,984 | Relevance Sensitive Non-Monotonic Inference on Belief Sequences | cs.AI | We present a method for relevance sensitive non-monotonic inference from
belief sequences which incorporates insights pertaining to prioritized
inference and relevance sensitive, inconsistency tolerant belief revision.
Our model uses a finite, logically open sequence of propositional formulas as
a representation for ... | computer science |
17,985 | Probabilistic Default Reasoning with Conditional Constraints | cs.AI | We propose a combination of probabilistic reasoning from conditional
constraints with approaches to default reasoning from conditional knowledge
bases. In detail, we generalize the notions of Pearl's entailment in system Z,
Lehmann's lexicographic entailment, and Geffner's conditional entailment to
conditional constrai... | computer science |
17,986 | A Compiler for Ordered Logic Programs | cs.AI | This paper describes a system, called PLP, for compiling ordered logic
programs into standard logic programs under the answer set semantics. In an
ordered logic program, rules are named by unique terms, and preferences among
rules are given by a set of dedicated atoms. An ordered logic program is
transformed into a sec... | computer science |
17,987 | SLDNFA-system | cs.AI | The SLDNFA-system results from the LP+ project at the K.U.Leuven, which
investigates logics and proof procedures for these logics for declarative
knowledge representation. Within this project inductive definition logic
(ID-logic) is used as representation logic. Different solvers are being
developed for this logic and ... | computer science |
17,988 | Logic Programs with Compiled Preferences | cs.AI | We describe an approach for compiling preferences into logic programs under
the answer set semantics. An ordered logic program is an extended logic program
in which rules are named by unique terms, and in which preferences among rules
are given by a set of dedicated atoms. An ordered logic program is transformed
into a... | computer science |
17,989 | Fuzzy Approaches to Abductive Inference | cs.AI | This paper proposes two kinds of fuzzy abductive inference in the framework
of fuzzy rule base. The abductive inference processes described here depend on
the semantic of the rule. We distinguish two classes of interpretation of a
fuzzy rule, certainty generation rules and possible generation rules. In this
paper we pr... | computer science |
17,990 | Problem solving in ID-logic with aggregates: some experiments | cs.AI | The goal of the LP+ project at the K.U.Leuven is to design an expressive
logic, suitable for declarative knowledge representation, and to develop
intelligent systems based on Logic Programming technology for solving
computational problems using the declarative specifications. The ID-logic is an
integration of typed cla... | computer science |
17,991 | Optimal Belief Revision | cs.AI | We propose a new approach to belief revision that provides a way to change
knowledge bases with a minimum of effort. We call this way of revising belief
states optimal belief revision. Our revision method gives special attention to
the fact that most belief revision processes are directed to a specific
informational ob... | computer science |
17,992 | cc-Golog: Towards More Realistic Logic-Based Robot Controllers | cs.AI | High-level robot controllers in realistic domains typically deal with
processes which operate concurrently, change the world continuously, and where
the execution of actions is event-driven as in ``charge the batteries as soon
as the voltage level is low''. While non-logic-based robot control languages
are well suited ... | computer science |
17,993 | Smodels: A System for Answer Set Programming | cs.AI | The Smodels system implements the stable model semantics for normal logic
programs. It handles a subclass of programs which contain no function symbols
and are domain-restricted but supports extensions including built-in functions
as well as cardinality and weight constraints. On top of this core engine more
involved s... | computer science |
17,994 | E-RES: A System for Reasoning about Actions, Events and Observations | cs.AI | E-RES is a system that implements the Language E, a logic for reasoning about
narratives of action occurrences and observations. E's semantics is
model-theoretic, but this implementation is based on a sound and complete
reformulation of E in terms of argumentation, and uses general computational
techniques of argumenta... | computer science |
17,995 | QUIP - A Tool for Computing Nonmonotonic Reasoning Tasks | cs.AI | In this paper, we outline the prototype of an automated inference tool,
called QUIP, which provides a uniform implementation for several nonmonotonic
reasoning formalisms. The theoretical basis of QUIP is derived from well-known
results about the computational complexity of nonmonotonic logics and exploits
a representa... | computer science |
17,996 | A Splitting Set Theorem for Epistemic Specifications | cs.AI | Over the past decade a considerable amount of research has been done to
expand logic programming languages to handle incomplete information. One such
language is the language of epistemic specifications. As is usual with logic
programming languages, the problem of answering queries is intractable in the
general case. F... | computer science |
17,997 | DES: a Challenge Problem for Nonmonotonic Reasoning Systems | cs.AI | The US Data Encryption Standard, DES for short, is put forward as an
interesting benchmark problem for nonmonotonic reasoning systems because (i) it
provides a set of test cases of industrial relevance which shares features of
randomly generated problems and real-world problems, (ii) the representation of
DES using nor... | computer science |
17,998 | Fages' Theorem and Answer Set Programming | cs.AI | We generalize a theorem by Francois Fages that describes the relationship
between the completion semantics and the answer set semantics for logic
programs with negation as failure. The study of this relationship is important
in connection with the emergence of answer set programming. Whenever the two
semantics are equi... | computer science |
17,999 | On the tractable counting of theory models and its application to belief
revision and truth maintenance | cs.AI | We introduced decomposable negation normal form (DNNF) recently as a
tractable form of propositional theories, and provided a number of powerful
logical operations that can be performed on it in polynomial time. We also
presented an algorithm for compiling any conjunctive normal form (CNF) into
DNNF and provided a stru... | computer science |
18,000 | BDD-based reasoning in the fluent calculus - first results | cs.AI | The paper reports on first preliminary results and insights gained in a
project aiming at implementing the fluent calculus using methods and techniques
based on binary decision diagrams. After reporting on an initial experiment
showing promising results we discuss our findings concerning various techniques
and heuristi... | computer science |
18,001 | Planning with Incomplete Information | cs.AI | Planning is a natural domain of application for frameworks of reasoning about
actions and change. In this paper we study how one such framework, the Language
E, can form the basis for planning under (possibly) incomplete information. We
define two types of plans: weak and safe plans, and propose a planner, called
the E... | computer science |
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