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• Invited Papers
• From AI to Systemic Knowledge Management
• MINERVA: A Tour-Guide Robot that Learns
• Dynamics, Morphology, and Materials in the Emergence of Cognition
• Natural Language Description of Image Sequences as a Form of Knowledge Representation
• Knowledge Discovery in Spatial Databases
• Cooperative Distributed Vision: Dynamic Integration of Visual Perception, Action, and Communication
• Technical Papers-Section 1
• Computing Probabilistic Least Common Subsumers in Description Logics
• Revising Nonmonotonic Theories: The Case of Defeasible Logic
• On the Translation of Qualitative Spatial Reasoning Problems into Modal Logics
• Following Conditional Structures of Knowledge
• Section 2
• A Theory of First-Order Counterfactual Reasoning
• Logic
• Based Choice of Projective Terms
• Knowledge Based Automatic Composition and Variation of Melodies for Minuets in Early Classical Style
• Inferring Flow of Control in Program Synthesis by Example
• Section 3
• Compilation Schemes: A Theoretical Tool for Assessing the Expressive Power of Planning Formalisms
• Generalized Cases: Representation and Steps Towards Efficient Similarity Assessment
• Be Busy and Unique
• or Be History
• The Utility Criterion for Removing Units in Self-Organizing Networks
• Section 4
• Development of Decision Support Algorithms for Intensive Care Medicine: A New Approach Combining Time Series Analysis and a Knowledge Base System with Learning and Revision Capabilities
• Object Recognition with Shape Prototypes in a 3D Construction Scenario
• Probabilistic, Prediction-Based Schedule Debugging for Autonomous Robot Office Couriers
• Section 5
• Collaborative Multi-robot Localization
• Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration
• A Flexible Architecture for Driver Assistance Systems
• Short Papers
• A Theory for Causal Reasoning
• Systematic vs. Local Search for SAT
• Information Environments for Software Agents
• Improving Reasoning Efficiency for Subclasses of Allen's Algebra with Instantiation Intervals
• Agents in Traffic Modelling
• From Reactive to Social Behaviour
• Time-Effect Relations of Medical Interventions in a Clinical Information System.

This book constitutes the refereed proceedings of the 23rd Annual German Conference on Artificial Intelligence, KI-99, held in Bonn, Germany, in September 1999. The volume opens with six full invited survey papers by internationally leading authorities on the subject. The 15 revised full papers and the six short papers presented were carefully reviewed and selected from 32 submissions received. Besides the established traditional topics addressed, special emphasis is placed on pattern recognition and robotics from an AI point of view.

• Flexible proof-replay with heuristics
• Flexible re-enactment of proofs
• Inference rights for controlling search in generating theorem provers
• A retrieval method for exploration of a case memory
• Incremental concept evolution based on adaptive feature weighting
• A 0-1 quadratic knapsack problem for modelizing and solving the constraint satisfaction problems
• An algorithm for solving systems of linear diophantine equations in naturals
• GenSAT: A navigational approach
• Timetabling using demand profiles
• Intelligent VR training
• Training strategies and knowledge acquisition: Using the same reflective tools for different purposes
• About the intended meaning of a linguistic negation
• Integration of inheritance in SNePS
• Measures of uncertainty and independence concept in different calculi
• A Multi-agent approach to first-order logic
• Modelling dynamic aspects of intentions
• Multi-agent negotiation algorithms for resources cost estimation: A case study
• Parla: A cooperation language for cognitive multi-agent systems
• Vivid agents arguing about distributed extended logic programs
• Approximate reasoning
• New results about sub-admissibility for general families of heuristic search algorithms
• Fixed point classification method for qualitative simulation
• Contextual logic of change and the ramification problem
• Temporal reasoning about actor programs
• A CLP model to the job sequencing problem
• A new approach for extracting rules from a trained neural network
• Bayesian networks, rule induction and logistic regression in the prediction of women survival suffering from breast cancer
• Controlling for unexpected goals when planning in a mixed-initiative setting
• Cooperative memory structures and commonsense knowledge for planning
• Diagonalization and type rewriting in clam
• Granularity for explanation
• Object model of intelligent tutoring shell
• Resource allocation on agent meta-societies
• Diagnostic information at your fingertips!
• Reasoning about actions with abductive logic programming
• Dimensions of embodiments: Possible futures for cognitive science (Abstract)
• Machine learning meets natural language (Abstract).

This book constitutes the refereed proceedings of the 8th Portuguese Conference on Artificial Intelligence, EPIA '97, held in Coimbra, Portugal, in October 1997. The volume presents 24 revised full papers and 9 revised posters selected from 74 submissions from various countries. Also included are two full invited papers and two abstracts of invited talks. The papers are organized in topical sections on automated reasoning and theorem proving; CBR and machine learning; constraints; intelligent tutoring; knowledge representation; multi-agent systems and DAI; nonmonotonic, qualitative and temporal reasoning, and problem solving.

This major collection of short essays reviews the scope and progress of research in artificial intelligence over the past two decades. Seminal and most-cited papers from the journal Artificial Intelligence are revisited by the authors who describe how their research has been developed, both by themselves and by others, since the journals first publication.The twenty-eight papers span a wide variety of domains, including truth maintainance systems and qualitative process theory, chemical structure analysis, diagnosis of faulty circuits, and understanding visual scenes; they also span a broad range of methodologies, from AI's mathematical foundations to systems architecture.The volume is dedicated to Allen Newell and concludes with a section of fourteen essays devoted to a retrospective on the strength and vision of his work.Sections/Contributors:- Artificial Intelligence in Perspective, D. G. Bobrow.- Foundations. J. McCarthy, R. C. Moore, A. Newell, N. J. Nilsson, J. Gordon and E. H. Shortliffe, J. Pearl, A. K. Mackworth and E. C. Freuder, J. de Kleer.- Vision. H. G. Barrow and J. M. Tenenbaum, B. K. P. Horn and B. Schunck, K. Ikeuchi, T. Kanade.- Qualitative Reasoning. J. de Kleer, K. D. Forbus, B. J. Kuipers, Y. Iwasake and H. A Simon.- Diagnosis. R. Davis, M. R. Genesereth, P. Szolovits and S. G. Pauker, R. Davis, B. G. Buchanan and E. H. Shortliffe, W. J. Clancey.- Architectures. J. S. Aikins, B. Hayes-Roth, M. J. Stefik et al.- Systems. R. E. Fikes and N. J. Nilsson, E. A Feigenbaum and B. G. Buchanan, J. McDermott. Allen Newell. H. A. Simon, M. J. Stefik and S. W. Smoliar, M. A. Arbib, D. C. Dennett, Purves, R. C. Schank and M. Y. Jona, P. S. Rosenbloom and J. E. Laird, P. E. Agre.
(source: Nielsen Book Data)

This major collection of short essays reviews the scope and progress of research in artificial intelligence over the past two decades. Seminal and most-cited papers from the journal Artificial Intelligence are revisited by the authors who describe how their research has been developed, both by themselves and by others, since the journals first publication.The twenty-eight papers span a wide variety of domains, including truth maintainance systems and qualitative process theory, chemical structure analysis, diagnosis of faulty circuits, and understanding visual scenes; they also span a broad range of methodologies, from AI's mathematical foundations to systems architecture.The volume is dedicated to Allen Newell and concludes with a section of fourteen essays devoted to a retrospective on the strength and vision of his work.Sections/Contributors:- Artificial Intelligence in Perspective, D. G. Bobrow.- Foundations. J. McCarthy, R. C. Moore, A. Newell, N. J. Nilsson, J. Gordon and E. H. Shortliffe, J. Pearl, A. K. Mackworth and E. C. Freuder, J. de Kleer.- Vision. H. G. Barrow and J. M. Tenenbaum, B. K. P. Horn and B. Schunck, K. Ikeuchi, T. Kanade.- Qualitative Reasoning. J. de Kleer, K. D. Forbus, B. J. Kuipers, Y. Iwasake and H. A Simon.- Diagnosis. R. Davis, M. R. Genesereth, P. Szolovits and S. G. Pauker, R. Davis, B. G. Buchanan and E. H. Shortliffe, W. J. Clancey.- Architectures. J. S. Aikins, B. Hayes-Roth, M. J. Stefik et al.- Systems. R. E. Fikes and N. J. Nilsson, E. A Feigenbaum and B. G. Buchanan, J. McDermott. Allen Newell. H. A. Simon, M. J. Stefik and S. W. Smoliar, M. A. Arbib, D. C. Dennett, Purves, R. C. Schank and M. Y. Jona, P. S. Rosenbloom and J. E. Laird, P. E. Agre.
(source: Nielsen Book Data)

• Computational research on interaction and agency / P.E. Agre
• Sensorimotor transformations in the worlds of frogs and robots / M.A. Arbib and J.-S. Liaw
• Learning to act using real-time dynamic programming / A.G. Barto, S.J. Bradtke, and S.P. Singh
• Learning dynamics: systems identification for perceptually challenged agents / K. Basye, T. Dean, and L.P. Kaelbling
• Dynamical systems perspective on agent-environment interaction / R.D. Beer
• On information invariants in robotics / B.R. Donald
• Stabilization of environments / K.J. Hammond, T.M. Converse, and J.W. Grass
• Architecture for adaptive intelligent systems / B. Hayes-Roth
• Analysis of adaptation and environment / I. Horswill
• Intelligent use of space / D. Kirsh
• Indexical knowledge and robot action-a logical account / Y. Lespérance and H.J. Levesque
• Exploiting patterns of interaction to achieve reactive behavior / D.M. Lyons and A.J. Hendriks
• Situated view of representation and control / S.J. Rosenschein and L.P. Kaelbling
• Use of dynamics in an intelligent controller for a space faring rescue robot / M. Schoppers
• On social laws for artificial agent societies: off-line design / Y. Shohaam and M. Tennenholtz
• Instructions, intentions, and expectations / B. Webber [and others]
• Reinforcement learning of non-markov decision processes / S.D. Whitehead and L.-J. Lin.

Over time the field of artificial intelligence has developed an "agent perspective" expanding its focus from thought to action, from search spaces to physical environments, and from problem-solving to long-term activity. Originally published as a special double volume of the journal Artificial Intelligence, this book brings together fundamental work by the top researchers in artificial intelligence, neural networks, computer science, robotics, and cognitive science on the themes of interaction and agency. It identifies recurring themes and outlines a methodology of the concept of "agency." The seventeen contributions cover the construction of principled characterizations of interactions between agents and their environments, as well as the use of these characterizations to guide analysis of existing agents and the synthesis of artificial agents.Artificial Intelligence series.Special Issues of Artificial Intelligence.
(source: Nielsen Book Data)

• Computational research on interaction and agency / P.E. Agre
• Sensorimotor transformations in the worlds of frogs and robots / M.A. Arbib and J.-S. Liaw
• Learning to act using real-time dynamic programming / A.G. Barto, S.J. Bradtke, and S.P. Singh
• Learning dynamics: systems identification for perceptually challenged agents / K. Basye, T. Dean, and L.P. Kaelbling
• Dynamical systems perspective on agent-environment interaction / R.D. Beer
• On information invariants in robotics / B.R. Donald
• Stabilization of environments / K.J. Hammond, T.M. Converse, and J.W. Grass
• Architecture for adaptive intelligent systems / B. Hayes-Roth
• Analysis of adaptation and environment / I. Horswill
• Intelligent use of space / D. Kirsh
• Indexical knowledge and robot action-a logical account / Y. Lespérance and H.J. Levesque
• Exploiting patterns of interaction to achieve reactive behavior / D.M. Lyons and A.J. Hendriks
• Situated view of representation and control / S.J. Rosenschein and L.P. Kaelbling
• Use of dynamics in an intelligent controller for a space faring rescue robot / M. Schoppers
• On social laws for artificial agent societies: off-line design / Y. Shohaam and M. Tennenholtz
• Instructions, intentions, and expectations / B. Webber [and others]
• Reinforcement learning of non-markov decision processes / S.D. Whitehead and L.-J. Lin.

Over time the field of artificial intelligence has developed an "agent perspective" expanding its focus from thought to action, from search spaces to physical environments, and from problem-solving to long-term activity. Originally published as a special double volume of the journal Artificial Intelligence, this book brings together fundamental work by the top researchers in artificial intelligence, neural networks, computer science, robotics, and cognitive science on the themes of interaction and agency. It identifies recurring themes and outlines a methodology of the concept of "agency." The seventeen contributions cover the construction of principled characterizations of interactions between agents and their environments, as well as the use of these characterizations to guide analysis of existing agents and the synthesis of artificial agents.Artificial Intelligence series.Special Issues of Artificial Intelligence.
(source: Nielsen Book Data)

• I Invited Papers.- Probabilistic Relational Models.- Inductive Databases.- Some Elements of Machine Learning.- II Contributed Papers.- Refinement Operators Can Be (Weakly) Perfect.- Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction.- Refining Complete Hypotheses in ILP.- Acquiring Graphic Design Knowledge with Nonmonotonic Inductive Learning.- Morphosyntactic Tagging of Slovene Using Progol.- Experiments in Predicting Biodegradability.- 1BC: A First-Order Bayesian Classifier.- Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive Logic Programming.- A Strong Complete Schema for Inductive Functional Logic Programming.- Application of Different Learning Methods to Hungarian Part-of-Speech Tagging.- Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraints.- Learning Word Segmentation Rules for Tag Prediction.- Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognition.- Rule Evaluation Measures: A Unifying View.- Improving Part of Speech Disambiguation Rules by Adding Linguistic Knowledge.- On Sufficient Conditions for Learnability of Logic Programs from Positive Data.- A Bounded Search Space of Clausal Theories.- Discovering New Knowledge from Graph Data Using Inductive Logic Programming.- Analogical Prediction.- Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms.- Theory Recovery.- Instance based function learning.- Some Properties of Inverse Resolution in Normal Logic Programs.- An Assessment of ILP-assisted models for toxicology and the PTE-3 experiment.
• (source: Nielsen Book Data)

Thisvolumecontains3invitedand24submittedpaperspresentedattheNi nth InternationalWorkshoponInductiveLogicProgramming, ILP-99. The24acc- tedpaperswereselectedbytheprogramcommitteefromthe40paperssubmitt ed toILP-99. Eachpaperwasreviewedbythreereferees, applyinghighreviewing standards. ILP-99washeldinBled, Slovenia, 24{27June1999. Itwascollocatedwith theSixteenthInternationalConferenceonMachineLearning, ICML-99, held27{ 30June1999. On27June, ILP-99andICML-99weregivenajointinvitedtalk byJ. RossQuinlanandajointpostersessionwhereallthepapersacceptedat ILP-99andICML-99werepresented. TheproceedingsofICML-99(editedby IvanBratkoandSa soD zeroski)arepublishedbyMorganKaufmann. WewishtothankalltheauthorswhosubmittedtheirpaperstoILP-99, the programcommitteemembersandotherreviewersfortheirhelpinselectinga high-qualityprogram, andtheinvitedspeakers:DaphneKoller, HeikkiMannila, andJ. RossQuinlan. ThanksareduetoTanjaUrban ci candherteamandMajda Zidanskiandherteamfortheorganizationalsupportprovided. Wewishtothank AlfredHofmannandAnnaKramerofSpringer-Verlagfortheircooperationin publishing these proceedings. Finally, we gratefully acknowledge the nancial supportprovidedbythesponsorsofILP-99. April1999 Sa soD zeroski PeterFlach ILP-99ProgramCommittee FrancescoBergadano(UniversityofTorino) HenrikBostr..om(UniversityofStockholm) IvanBratko(UniversityofLjubljana) WilliamCohen(AT&TResearchLabs) JamesCussens(UniversityofYork) LucDeRaedt(UniversityofLeuven) Sa soD zeroski(Jo zefStefanInstitute, co-chair) PeterFlach(UniversityofBristol, co-chair) AlanFrisch(UniversityofYork) KoichiFurukawa(KeioUniversity) RoniKhardon(UniversityofEdinburgh) NadaLavra c(Jo zefStefanInstitute) JohnLloyd(AustralianNationalUniversity) StanMatwin(UniversityofOttawa) RaymondMooney(UniversityofTexas) StephenMuggleton(UniversityofYork) Shan-HweiNienhuys-Cheng(UniversityofRotterdam) DavidPage(UniversityofLouisville) BernhardPfahringer(AustrianResearchInstituteforAI) CelineRouveirol(UniversityofParis) ClaudeSammut(UniversityofNewSouthWales) MicheleSebag(EcolePolytechnique) AshwinSrinivasan(UniversityofOxford) PrasadTadepalli(OregonStateUniversity) StefanWrobel(GMDResearchCenterforInformationTechnology) OrganizationalSupport TheAlbatrossCongressTouristAgency, Bled Center for Knowledge Transfer in Information Technologies, Jo zef Stefan Institute, Ljubljana SponsorsofILP-99 ILPnet2, NetworkofExcellenceinInductiveLogicProgramming COMPULOGNet, EuropeanNetworkofExcellenceinComputationalLogic Jo zefStefanInstitute, Ljubljana LPASoftware, Inc. UniversityofBristol TableofContents I InvitedPapers ProbabilisticRelationalModels D. Koller ...3 InductiveDatabases(Abstract) H. Mannila...14 SomeElementsofMachineLearning(ExtendedAbstract) J. R. Quinlan...15 II ContributedPapers Re nementOperatorsCanBe(Weakly)Perfect L. Badea, M. Stanciu...21 CombiningDivide-and-ConquerandSeparate-and-ConquerforE cientand E ectiveRuleInduction H. Bostr..om, L. Asker...33 Re ningCompleteHypothesesinILP I. Bratko...44 AcquiringGraphicDesignKnowledge withNonmonotonicInductiveLearning K. Chiba, H. Ohwada, F. Mizoguchi...56 MorphosyntacticTaggingofSloveneUsingProgol J. Cussens, S. D zeroski, T. Erjavec ...68 ExperimentsinPredictingBiodegradability S. D zeroski, H. Blockeel, B. Kompare, S. Kramer, B. Pfahringer, W. VanLaer ...80 1BC:AFirst-OrderBayesianClassi er P. Flach, N. Lachiche...92 SortedDownwardRe nement:BuildingBackgroundKnowledge intoaRe nementOperatorforInductiveLogicProgramming A. M. Frisch ...104 AStrongCompleteSchemaforInductiveFunctionalLogicProgramming J. Hern andez-Orallo, M. J. Ram rez-Quintana...116 ApplicationofDi erentLearningMethods toHungarianPart-of-SpeechTagging T. Horv ath, Z. Alexin, T. Gyim othy, S. Wrobel ...1 28 VIII TableofContents CombiningLAPISandWordNetfortheLearningofLRParserswith OptimalSemanticConstraints D. Kazakov...140 LearningWordSegmentationRulesforTagPrediction D. Kazakov, S. Manandhar, T. Erjavec ...152 ApproximateILPRulesbyBackpropagationNeuralNetwork: AResultonThaiCharacterRecognition B. Kijsirikul, S. Sinthupinyo...162 RuleEvaluationMeasures:AUnifyingView N. Lavra c, P. Flach, B. Zupan...174 ImprovingPart-of-SpeechDisambiguationRulesbyAdding LinguisticKnowledge N. Lindberg, M. Eineborg ...186 OnSu cientConditionsforLearnabilityofLogicProgramsfrom PositiveData E. Martin, A. Sharma ...198 ABoundedSearchSpaceofClausalTheories H. Midelfart...210 DiscoveringNewKnowledgefromGraphData UsingInductiveLogicProgramming T. Miyahara, T. Shoudai, T. Uchida, T. Kuboyama, K. Takahashi, H. Ueda...
(source: Nielsen Book Data)

• Attribute-value learning versus inductive logic programming: The missing links
• Advances in ILP theory and implementations
• Application of ILP to problems in chemistry and biology
• Relational reinforcement learning
• Learning first-order acyclic Horn programs from entailment
• Combining statistical and relational methods for learning in hypertext domains
• Application of inductive logic programming to discover rules governing the three-dimensional topology of protein structure
• Term comparisons in first-order similarity measures
• Stochastic propositionalization of non-determinate background knowledge
• A stochastic simple similarity
• Using prior probabilities and density estimation for relational classification
• Induction of Constraint Grammar-rules using Progol
• A hybrid approach to word segmentation
• Learning multilingual morphology with Clog
• Using ILP-systems for verification and validation of multi-agent systems
• Inducing shogi heuristics using inductive logic programming
• Repeat learning using predicate invention
• Normal programs and multiple predicate learning
• Strongly typed inductive concept learning
• Function-free Horn clauses are hard to approximate
• DOGMA: A GA-based relational learner
• Generalization under implication by ?-subsumption
• Prolog, refinements and RLGG's
• Learning structurally indeterminate clauses
• Completing Inverse Entailment
• Distances and limits on Herbrand interpretations
• Relational distance-based clustering
• A framework for defining distances between first-order logic objects
• Detecting traffic problems with ILP
• A comparison of ILP and propositional systems on propositional traffic data.

This book constitutes the refereed proceedings of the 8th International Conference on Inductive Logic Programming, ILP-98, held in Madison, Wisconsin, USA, in July 1998. The 27 revised full papers presented together with the abstracts of three invited talks were carefully reviewed and selected for inclusion in the book. All relevant aspects of inductive logic programming are covered ranging from theory to implementations and applications.

• How to construct a logic for your application.- A model elimination calculus with built-in theories.- A new sorted logic.- An explanatory framework for human theorem proving.- Towards first-order deduction based on Shannon graphs.- Success and failure of expert systems in. different fields of industrial application.- Viewing knowledge engineering as a symbiosis of Modeling to make sense and modeling to implement systems.- Cases as a basis for knowledge acquisition in the pre-formal phases of knowledge engineering.- Controlling generate & test in any time.- Efficient computation of solutions for contradictory time interval networks.- Extensions of concept languages for a mechanical engineering application.- Combining terminological and rule-based reasoning for abstraction processes.- Forward logic evaluation: Compiling a partially evaluated meta-interpreter into the WAM.- Concept support as a method for programming neural networks with symbolic knowledge.- A heuristic inductive generalization method and its application to VLSI-design.- Learning plan abstractions.- On discontinuous Q-Functions in reinforcement learning.- An intelligent tutoring system for classification problem solving.- Knowledge-based processing of medical language: A language engineering approach.- Text planning in ITEX: A hybrid approach.- Yes/no questions with negation: Towards integrating semantics and pragmatics.- An efficient decision algorithm for feature logic.- Universally quantified queries in languages with order-sorted logics.- A semantic view of explanation.- Goal-driven similarity assessment.- Delegated negotiation for resource re-allocation.- Towards a specification language for cooperation methods.- Improving operating system usage.- The role of user models for conflicts in a constraint-based model of generation.- Criteria in natural language generation: Minimal criteria and their impacts.- Terminological representation, natural language & relation algebra.- Linking humans and intelligent systems or: What are user agents good for?.- An advisor for the management of the acute radiation syndrome.
• (source: Nielsen Book Data)

This volume gives the proceedings of the sixteenth German Conference on Artificial Intelligence, held in the Gustav Stresemann Institute in Berlin from August 31 to September 3, 1992. The volume contains 24 papers presentedin the technical sessions, 8 papers selected from the workshop contributions, and an invited talk by D.M. Gabbay entitled "Howto construct a logic for your application". Topics discussed in the technical papers include: a model elimination calculus, a sorted logic, human theorem proving, deduction based on Shannon graphs, expert system applications, knowledge engineering, time interval networks, forward logic evaluation, concept support, heuristic inductivegeneralization, language engineering, feature logic, similarity assessment, and many others.
(source: Nielsen Book Data)

Dealing with the theme of "prospects for artificial inteligence as the general science of intelligence", this work covers a wide range of topics. It attempts to identify trends and projects into the future, instead of simply surveying past achievements. The editor first introduces attempts to define the design-based approach to the study of intelligence, that characterizes IA. Following this, contributing papers report on work already done. Thus the subjects discussed include natural language, evolutionary AI, robotics, game playing, tutoring systems, cognitive modelling, SOAR, neural nets, and the modelling of motivation and emotions.
(source: Nielsen Book Data)

• Knowledge representation: Features of knowledge
• Deduction and computation
• An introduction to automated deduction
• Fundamental mechanisms in machine learning and inductive inference
• Methods of automated reasoning
• Term rewriting as a basis for the design of a functional and parallel programming language
• Concurrent Prolog: A progress report.

This volume contains the elaborated and harmonized versions of seven lectures given at the first Advanced Course in Artificial Intelligence, held in Vignieu, France, in July 1985. Most of them were written in tutorial form; the book thus provides an extremely valuable guide to the fundamental aspects of AI. In the first part, Delgrande and Mylopoulos discuss the concept of knowledge and its representation. The second part is devoted to the processing of knowledge. The contribution by Huet shows that both computation and inference or deduction are just different aspects of the same phenomenon. The chapter written by Stickel gives a thorough and knowledgeable introduction to the most important aspects of deduction by some form of resolution. The kind of reasoning that is involved in inductive inference problem solving (or programming) from examples, and in learning, is covered by Biermann. The tutorial by Bibel covers the more important forms of knowledge processing that might play a significant role in common sense reasoning. The third part of the book focuses on logic programming and functional programming. Jorrand presents the language FP2, where term rewriting forms the basis for the semantics of both functional and parallel programming. In the last chapter, Shapiro gives an overview of the current state of concurrent PROLOG.

• Sokoban: Evaluating standard single-agent search techniques in the presence of deadlock
• A heuristic incremental modeling approach to course timetabling
• Planning strategy representation in DoLittle
• Establishing logical connectivity between query keywords and database contents
• Test-driving tanka: Evaluating a semi-automatic system of text analysis for knowledge acquisition
• An object indexing methodology as support to object recognition
• Oracles and assistants: Machine learning applied to network supervision
• A common multi-agent testbed for diverse seamless personal information networking applications
• A hybrid genetic algorithm for the vehicle routing problem with time windows
• Maintaining genetic diversity in genetic algorithms through co-evolution
• The effect of genetic operator probabilities and selection strategies on the performance of a genetic algorithm
• The impact of external dependency in genetic programming primitives
• Quality control in the concept learning process
• Grapheme generation in learning to read english words
• A trainable bracketer for noun modifiers
• Lessons learned in the development and implementation of a bilingual nationally accessible knowledge-based system
• Selecting the next action with constraints
• Poker as a testbed for AI research
• Fault prediction in the telephone access loop using a neural network
• Learning english syllabification rules
• Characterizing tractable CSPs
• An attribute redundancy measure for clustering
• On the complexity of VLSI-friendly neural networks for classification problems
• String clustering and statistical validation of clusters
• Finding partitions for learning control of dynamic systems
• A relational modeling of cognitive maps
• Distance constraint arrays: A model for reasoning on intervals with qualitative and quantitative distances
• Revising Timegraph-II
• Temporally invariant junction tree for inference in dynamic bayesian network
• Utility theory-based user models for intelligent interface agents
• A hybrid convergent method for learning probabilistic networks
• Relational concepts and the fourier transform: An empirical study
• ELEM2: A learning system for more accurate classifications
• Sequential instance-based learning
• Predicate invention from a few examples.

This book constitutes the refereed proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'98, held in Vancouver, BC, Canada in June 1998. The 28 revised full papers presented together with 10 extended abstracts were carefully reviewed and selected from a total of more than twice as many submissions. The book is divided in topical sections on planning, constraints, search and databases; applications; genetic algorithms; learning and natural language; reasoning; uncertainty; and learning.

• Inductive logic programming for natural language processing.- An initial experiment into stereochemistry-based drug design using inductive logic programming.- Applying ILP to diterpene structure elucidation from 13C NMR spectra.- Analysis and prediction of piano performances using inductive logic programming.- Noise detection and elimination applied to noise handling in a KRK chess endgame.- Feature construction with inductive logic programming: A study of quantitative predictions of biological activity by structural attributes.- Polynomial-time learning in logic programming and constraint logic programming.- Analyzing and learning ECG waveforms.- Learning rules that classify ocular fundus images for glaucoma diagnosis.- A new design and implementation of progol by bottom-up computation.- Inductive logic program synthesis with DIALOGS.- Relational knowledge discovery in databases.- Efficient ?-subsumption based on graph algorithms.- Integrity constraints in ILP using a Monte Carlo approach.- Restructuring chain datalog programs.- Top-down induction of logic programs from incomplete samples.- Least generalizations under implication.- Efficient proof encoding.- Learning Logic programs with random classification noise.- Handling Quantifiers in ILP.- Learning from positive data.- ?-Subsumption and its application to learning from positive-only examples.
• (source: Nielsen Book Data)

This book constitutes the strictly refereed post-workshop proceedings of the 6th International Workshop on Inductive Logic Programming, ILP-96, held in Stockholm, Sweden, in August 1996. The 21 full papers were carefully reviewed and selected for inclusion in the book in revised version. Also included is the invited contribution "Inductive logic programming for natural language processing" by Raymond J. Mooney. Among the topics covered are natural language learning, drug design, NMR and ECG analysis, glaucoma diagnosis, efficiency measures for implementations and database interaction, program synthesis, proof encoding and learning in the absence of negative data, and least generalizations under implication ordering.
(source: Nielsen Book Data)

• Knowledge discovery in databases: An overview.- On the complexity of some Inductive Logic Programming problems.- Inductive logic programming and constraint logic programming (abstract).- Learning phonetic rules in a speech recognition system.- Cautious induction in inductive logic programming.- Generating numerical literals during refinement.- Lookahead and discretization in ILP.- Data mining via ILP: The application of Progol to a database of enantioseparations.- Part-of-speech tagging using Progol.- Maximum Entropy modeling with Clausal Constraints.- Mining association rules in multiple relations.- Using logical decision trees for clustering.- Induction of Slovene nominal paradigms.- Normal forms for inductive logic programming.- On a sufficient condition for the existence of most specific hypothesis in progol.- Induction of logic programs with more than one recursive clause by analyzing saturations.- A logical framework for graph theoretical decision tree learning.- Learning with abduction.- Systematic Predicate Invention in Inductive Logic Programming.- Learning programs in the event calculus.- Distance between Herbrand interpretations: A measure for approximations to a target concept.- Realizing Progol by forward reasoning.- Probabilistic first-order classification.- Learning Horn definitions with equivalence and membership queries.- Using abstraction schemata in inductive logic programming.- Distance induction in first order logic.- Carcinogenesis predictions using ILP.- Discovery of first-order regularities in a relational database using ofine candidate determination.- Which hypotheses can be found with inverse entailment?.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th International Workshop on Inductive Logic Programming, ILP-97, held in Prague, Czech Republic, in September 1997. The volume presents revised versions of nine papers in long version and 17 short papers accepted after a thorough reviewing process. Also included are three invited papers by Usama Fayyad, Jean-Francois Puget, and Georg Gottlob. Among the topics addressed are various logic programming issues, natural language processing, speech processing, abductive learning, data mining, knowledge discovery, and relational database systems.
(source: Nielsen Book Data)

• Machine generated contents note:
• I. Agents in the World: What are Agents and How Can They be Built?
• 1. Artificial Intelligence and Agents
• 1.1. What is Artificial Intelligence?
• 1.1.1. Artificial and Natural Intelligence
• 1.2. A Brief History of Artificial Intelligence
• 1.2.1. Relationship to Other Disciplines
• 1.3. Agents Situated in Environments
• 1.4. Designing Agents
• 1.4.1. Design Time, Offline and Online Computation
• 1.4.2. Tasks
• 1.4.3. Defining a Solution
• 1.4.4. Representations
• 1.5. Agent Design Space
• 1.5.1. Modularity
• 1.5.2. Planning Horizon
• 1.5.3. Representation
• 1.5.4. Computational Limits
• 1.5.5. Learning
• 1.5.6. Uncertainty
• 1.5.7. Preference
• 1.5.8. Number of Agents
• 1.5.9. Interaction
• 1.5.10. Interaction of the Dimensions
• 1.6. Prototypical Applications
• 1.6.1. An Autonomous Delivery Robot
• 1.6.2. A Diagnostic Assistant
• 1.6.3. An Intelligent Tutoring System
• 1.6.4. A Trading Agent
• 1.6.5. Smart House
• 1.7. Overview of the Book
• 1.8. Review
• 1.9. References and Further Reading
• 1.10. Exercises
• 2. Agent Architectures and Hierarchical Control
• 2.1. Agents
• 2.2. Agent Systems
• 2.2.1. The Agent Function
• 2.3. Hierarchical Control
• 2.4. Acting with Reasoning
• 2.4.1. Agents Modeling the World
• 2.4.2. Knowledge and Acting
• 2.4.3. Design Time and Offline Computation
• 2.4.4. Online Computation
• 2.5. Review
• 2.6. References and Further Reading
• 2.7. Exercises
• II. Reasoning, Planning and Learning with Certainty
• 3. Searching for Solutions
• 3.1. Problem Solving as Search
• 3.2. State Spaces
• 3.3. Graph Searching
• 3.3.1. Formalizing Graph Searching
• 3.4. A Generic Searching Algorithm
• 3.5. Uninformed Search Strategies
• 3.5.1. Breadth-First Search
• 3.5.2. Depth-First Search
• 3.5.3. Iterative Deepening
• 3.5.4. Lowest-Cost-First Search
• 3.6. Heuristic Search
• 3.6.1. A* Search
• 3.6.2. Designing a Heuristic Function
• 3.7. Pruning the Search Space
• 3.7.1. Cycle Pruning
• 3.7.2. Multiple-Path Pruning
• 3.7.3. Summary of Search Strategies
• 3.8. More Sophisticated Search
• 3.8.1. Branch and Bound
• 3.8.2. Direction of Search
• 3.8.3. Dynamic Programming
• 3.9. Review
• 3.10. References and Further Reading
• 3.11. Exercises
• 4. Reasoning with Constraints
• 4.1. Possible Worlds, Variables, and Constraints
• 4.1.1. Variables and Worlds
• 4.1.2. Constraints
• 4.1.3. Constraint Satisfaction Problems
• 4.2. Generate-and-Test Algorithms
• 4.3. Solving CSPs Using Search
• 4.4. Consistency Algorithms
• 4.5. Domain Splitting
• 4.6. Variable Elimination
• 4.7. Local Search
• 4.7.1. Iterative Best Improvement
• 4.7.2. Randomized Algorithms
• 4.7.3. Local Search Variants
• 4.7.4. Evaluating Randomized Algorithms
• 4.7.5. Random Restart
• 4.8. Population-Based Methods
• 4.9. Optimization
• 4.9.1. Systematic Methods for Optimization
• 4.9.2. Local Search for Optimization
• 4.10. Review
• 4.11. References and Further Reading
• 4.12. Exercises
• 5. Propositions and Inference
• 5.1. Propositions
• 5.1.1. Syntax of Propositional Calculus
• 5.1.2. Semantics of the Propositional Calculus
• 5.2. Propositional Constraints
• 5.2.1. Clausal Form for Consistency Algorithms
• 5.2.2. Exploiting Propositional Structure in Local Search
• 5.3. Propositional Definite Clauses
• 5.3.1. Questions and Answers
• 5.3.2. Proofs
• 5.4. Knowledge Representation Issues
• 5.4.1. Background Knowledge and Observations
• 5.4.2. Querying the User
• 5.4.3. Knowledge-Level Explanation
• 5.4.4. Knowledge-Level Debugging
• 5.5. Proving by Contradiction
• 5.5.1. Horn Clauses
• 5.5.2. Assumables and Conflicts
• 5.5.3. Consistency-Based Diagnosis
• 5.5.4. Reasoning with Assumptions and Horn Clauses
• 5.6. Complete Knowledge Assumption
• 5.6.1. Non-monotonic Reasoning
• 5.6.2. Proof Procedures for Negation as Failure
• 5.7. Abduction
• 5.8. Causal Models
• 5.9. Review
• 5.10. References and Further Reading
• 5.11. Exercises
• 6. Planning with Certainty
• 6.1. Representing States, Actions, and Goals
• 6.1.1. Explicit State-Space Representation
• 6.1.2. The STRIPS Representation
• 6.1.3. Feature-Based Representation of Actions
• 6.1.4. Initial States and Goals
• 6.2. Forward Planning
• 6.3. Regression Planning
• 6.4. Planning as a CSP
• 6.4.1. Action Features
• 6.5. Partial-Order Planning
• 6.6. Review
• 6.7. References and Further Reading
• 6.8. Exercises
• 7. Supervised Machine Learning
• 7.1. Learning Issues
• 7.2. Supervised Learning
• 7.2.1. Evaluating Predictions
• 7.2.2. Types of Errors
• 7.2.3. Point Estimates with No Input Features
• 7.3. Basic Models for Supervised Learning
• 7.3.1. Learning Decision Trees
• 7.3.2. Linear Regression and Classification
• 7.4. Overfitting
• 7.4.1. Pseudocounts
• 7.4.2. Regularization
• 7.4.3. Cross Validation
• 7.5. Neural Networks and Deep Learning
• 7.6. Composite Models
• 7.6.1. Random Forests
• 7.6.2. Ensemble Learning
• 7.7. Case-Based Reasoning
• 7.8. Learning as Refining the Hypothesis Space
• 7.8.1. Version-Space Learning
• 7.8.2. Probably Approximately Correct Learning
• 7.9. Review
• 7.10. References and Further Reading
• 7.11. Exercises
• III. Reasoning, Learning and Acting with Uncertainty
• 8. Reasoning with Uncertainty
• 8.1. Probability
• 8.1.1. Semantics of Probability
• 8.1.2. Axioms for Probability
• 8.1.3. Conditional Probability
• 8.1.4. Expected Values
• 8.1.5. Information
• 8.2. Independence
• 8.3. Belief Networks
• 8.3.1. Observations and Queries
• 8.3.2. Constructing Belief Networks
• 8.4. Probabilistic Inference
• 8.4.1. Variable Elimination for Belief Networks
• 8.4.2. Representing Conditional Probabilities and Factors
• 8.5. Sequential Probability Models
• 8.5.1. Markov Chains
• 8.5.2. Hidden Markov Models
• 8.5.3. Algorithms for Monitoring and Smoothing
• 8.5.4. Dynamic Belief Networks
• 8.5.5. Time Granularity
• 8.5.6. Probabilistic Models of Language
• 8.6. Stochastic Simulation
• 8.6.1. Sampling from a Single Variable
• 8.6.2. Forward Sampling in Belief Networks
• 8.6.3. Rejection Sampling
• 8.6.4. Likelihood Weighting
• 8.6.5. Importance Sampling
• 8.6.6. Particle Filtering
• 8.6.7. Markov Chain Monte Carlo
• 8.7. Review
• 8.8. References and Further Reading
• 8.9. Exercises
• 9. Planning with Uncertainty
• 9.1. Preferences and Utility
• 9.1.1. Axioms for Rationality
• 9.1.2. Factored Utility
• 9.1.3. Prospect Theory
• 9.2. One-Off Decisions
• 9.2.1. Single-Stage Decision Networks
• 9.3. Sequential Decisions
• 9.3.1. Decision Networks
• 9.3.2. Policies
• 9.3.3. Variable Elimination for Decision Networks
• 9.4. The Value of Information and Control
• 9.5. Decision Processes
• 9.5.1. Policies
• 9.5.2. Value Iteration
• 9.5.3. Policy Iteration
• 9.5.4. Dynamic Decision Networks
• 9.5.5. Partially Observable Decision Processes
• 9.6. Review
• 9.7. References and Further Reading
• 9.8. Exercises
• 10. Learning with Uncertainty
• 10.1. Probabilistic Learning
• 10.1.1. Learning Probabilities
• 10.1.2. Probabilistic Classifiers
• 10.1.3. MAP Learning of Decision Trees
• 10.1.4. Description Length
• 10.2. Unsupervised Learning
• 10.2.1. k-Means
• 10.2.2. Expectation Maximization for Soft Clustering
• 10.3. Learning Belief Networks
• 10.3.1. Learning the Probabilities
• 10.3.2. Hidden Variables
• 10.3.3. Missing Data
• 10.3.4. Structure Learning
• 10.3.5. General Case of Belief Network Learning
• 10.4. Bayesian Learning
• 10.5. Review
• 10.6. References and Further Reading
• 10.7. Exercises
• 11. Multiagent Systems
• 11.1. Multiagent Framework
• 11.2. Representations of Games
• 11.2.1. Normal Form Games
• 11.2.2. Extensive Form of a Game
• 11.2.3. Multiagent Decision Networks
• 11.3. Computing Strategies with Perfect Information
• 11.4. Reasoning with Imperfect Information
• 11.4.1. Computing Nash Equilibria
• 11.5. Group Decision Making
• 11.6. Mechanism Design
• 11.7. Review
• 11.8. References and Further Reading
• 11.9. Exercises
• 12. Learning to Act
• 12.1. Reinforcement Learning Problem
• 12.2. Evolutionary Algorithms
• 12.3. Temporal Differences
• 12.4. Q-learning
• 12.5. Exploration and Exploitation
• 12.6. Evaluating Reinforcement Learning Algorithms
• 12.7. On-Policy Learning
• 12.8. Model-Based Reinforcement Learning
• 12.9. Reinforcement Learning with Features
• 12.9.1. SARSA with Linear Function Approximation
• 12.10. Multiagent Reinforcement Learning
• 12.10.1. Perfect-Information Games
• 12.10.2. Learning to Coordinate
• 12.11. Review
• 12.12. References and Further Reading
• 12.13. Exercises

• Note continued:
• IV. Reasoning, Learning and Acting with Individuals and Relations
• 13. Individuals and Relations
• 13.1. Exploiting Relational Structure
• 13.2. Symbols and Semantics
• 13.3. Datalog: A Relational Rule Language
• 13.3.1. Semantics of Ground Datalog
• 13.3.2. Interpreting Variables
• 13.3.3. Queries with Variables
• 13.4. Proofs and Substitutions
• 13.4.1. Instances and Substitutions
• 13.4.2. Bottom-up Procedure with Variables
• 13.4.3. Unification
• 13.4.4. Definite Resolution with Variables
• 13.5. Function Symbols
• 13.5.1. Proof Procedures with Function Symbols
• 13.6. Applications in Natural Language
• 13.6.1. Using Definite Clauses for Context-Free Grammars
• 13.6.2. Augmenting the Grammar
• 13.6.3. Building Structures for Non-terminals
• 13.6.4. Canned Text Output
• 13.6.5. Enforcing Constraints
• 13.6.6. Building a Natural Language Interface to a Database
• 13.6.7. Limitations
• 13.7. Equality
• 13.7.1. Allowing Equality Assertions
• 13.7.2. Unique Names Assumption
• 13.8. Complete Knowledge Assumption
• 13.8.1. Complete Knowledge Assumption Proof Procedures
• 13.9. Review
• 13.10. References and Further Reading
• 13.11. Exercises
• 14. Ontologies and Knowledge-Based Systems
• 14.1. Knowledge Sharing
• 14.2. Flexible Representations
• 14.2.1. Choosing Individuals and Relations
• 14.2.2. Graphical Representations
• 14.2.3. Classes
• 14.3. Ontologies and Knowledge Sharing
• 14.3.1. Uniform Resource Identifiers
• 14.3.2. Description Logic
• 14.3.3. Top-Level Ontologies
• 14.4. Implementing Knowledge-Based Systems
• 14.4.1. Base Languages and Metalanguages
• 14.4.2. A Vanilla Meta-Interpreter
• 14.4.3. Expanding the Base Language
• 14.4.4. Depth-Bounded Search
• 14.4.5. Meta-Interpreter to Build Proof Trees
• 14.4.6. Delaying Goals
• 14.5. Review
• 14.6. References and Further Reading
• 14.7. Exercises
• 15. Relational Planning, Learning, and Probabilistic Reasoning
• 15.1. Planning with Individuals and Relations
• 15.1.1. Situation Calculus
• 15.1.2. Event Calculus
• 15.2. Relational Learning
• 15.2.1. Structure Learning: Inductive Logic Programming
• 15.2.2. Learning Hidden Properties: Collaborative Filtering
• 15.3. Statistical Relational Artificial Intelligence
• 15.3.1. Relational Probabilistic Models
• 15.4. Review
• 15.5. References and Further Reading
• 15.6. Exercises
• V. Retrospect and Prospect
• 16. Retrospect and Prospect
• 16.1. Dimensions of Complexity Revisited
• 16.2. Social and Ethical Consequences
• 16.3. References and Further Reading
• 16.4. Exercises
• A. Mathematical Preliminaries and Notation
• A.1. Discrete Mathematics
• A.2. Functions, Factors and Arrays
• A.3. Relations and the Relational Algebra. A.1. Discrete Mathematics
• A.2. Functions, Factors and Arrays
• A.3. Relations and the Relational Algebra.

Artificial intelligence, including machine learning, has emerged as a transformational science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents presents AI using a coherent framework to study the design of intelligent computational agents. By showing how the basic approaches fit into a multidimensional design space, readers learn the fundamentals without losing sight of the bigger picture. The new edition also features expanded coverage on machine learning material, as well as on the social and ethical consequences of AI and ML. The book balances theory and experiment, showing how to link them together, and develops the science of AI together with its engineering applications. Although structured as an undergraduate and graduate textbook, the book's straightforward, self-contained style will also appeal to an audience of professionals, researchers, and independent learners. The second edition is well-supported by strong pedagogical features and online resources to enhance student comprehension.
(source: Nielsen Book Data)

• Hyper tableaux
• An algorithm for the retrieval of unifiers from discrimination trees
• Building proofs or counterexamples by analogy in a resolution framework
• What you always wanted to know about rigid E-unification
• Labelled proofs for quantified modal logic
• A uniform tableaux method for nonmonotonic modal logics
• Design and implementation of diagnostic strategies using modal logic
• A modal action logic based framework for organization specification and analysis
• McCarthy's idea
• Strong and explicit negation in non-monotonic reasoning and logic programming
• Only persistence makes nonmonotonicity monotonous
• Ordering-based representations of rational inference
• Semi-representability of default theories in rational default logic
• A query answering algorithm for Lukaszewicz' general open default theory
• Infinitary default logic for specification of nonmonotonic reasoning
• A system for computing constrained default logic extensions
• The Oz programming model
• An abductive framework for negation in disjunctive logic programming
• Characterizing D-WFS: Confluence and iterated GCWA
• Modules and specifications
• Logic programming with integrity constraints
• Temporal reasoning over linear discrete time
• Similarity saturation for first order linear temporal logic with UNLESS
• Carving Up space: Steps towards construction of an absolutely complete theory of spatial regions
• Informational logic for automated reasoning
• Extensions for open default theories via the domain closure assumption
• Revising and updating using a back-up semantics
• A simple signed system for paraconsistent reasoning.

This book presents the refereed proceedings of the Sixth European Workshop on Logics in Artificial Intelligence, JELIA '96, held in Evora, Portugal in September/October 1996. The 25 revised full papers included together with three invited papers were selected from 57 submissions. Many relevant aspects of AI logics are addressed. The papers are organized in sections on automated reasoning, modal logics, applications, nonmonotonic reasoning, default logics, logic programming, temporal and spatial logics, and belief revision and paraconsistency.

• Introduction
• 1. Intelligent Behavior
• 2. Rational Behavior
• 3. Utility
• 4. State and Cost
• 5. Design and Decision
• 6. Motivation and Autonomy
• 7. Goals and Behavior
• 8. Accomplishing Tasks
• 9. Prerequisites for an Autonomous Robot
• 10. The Goal Function in Robot Architecture
• 11. Animal and Robot Learning
• 12. Conclusions
• Bibliography
• Index.

This exciting study explores the novel insight, based on well-established ethological principles, that animals, humans, and autonomous robots can all be analyzed as multi-task autonomous control systems. Intelligence takes many forms. This exciting study explores the novel insight, based on well-established ethological principles, that animals, humans, and autonomous robots can all be analyzed as multi-task autonomous control systems. Biological adaptive systems, the authors argue, can in fact provide a better understanding of intelligence and rationality than that provided by traditional AI. In this technically sophisticated, clearly written investigation of robot-animal analogies, McFarland and Boesser show that a bee's accuracy in navigating on a cloudy day and a moth's simple but effective hearing mechanisms have as much to teach us about intelligent behavior as human models. In defining intelligent behavior, what matters is the behavioral outcome, not the nature of the mechanism by which the outcome is achieved. Similarly, in designing robots capable of intelligent behavior, what matters is the behavioral outcome. McFarland and Boesser address the problem of how to assess the consequences of robot behavior in a way that is meaningful in terms of the robot's intended role, comparing animal and robot in relation to rational behavior, goal seeking, task accomplishment, learning, and other important theoretical issues.
(source: Nielsen Book Data)

• Solving linear constraints on finite domains through parsing
• Constraint solving in finite domains under user control
• A new method for solving linear constraints on the natural numbers
• A constraint-based fuzzy inference system
• A constraint-based language for querying taxonomic systems
• Heuristic parsing and search space pruning
• Wave-shaping in multiprocessor bidirectional heuristic state space search
• The extended stable models of contradiction removal semantics
• Modeling a rational cognitive agent in SNePS
• Semantics of property inheritance in a hierarchic system with explicit negation
• Time in confluences: Dealing with delays for consistency-checking
• A temporal representation for imperatively structured plans of actions
• Maximal intervals: An approach to temporal reasoning
• Consistency driven planning
• An efficient approach to planning in assembly tasks
• Towards a theory of the repair process
• Declarative source debugging
• A neural approach to data compression and classification
• Generalization for a propositional calculus: a constraints-based approach
• Adaptive learning using a qualitative feedback loop.

The Portuguese Association for Artificial Intelligence has been organizing Portuguese Conferences on Artificial Intelligence, now held every second year, since 1985. This volume contains selected papers from the Fifth Conference on Artificial Intelligence. The conference has an international status: 62 contributions from 13 countries were received, of which 26 were from Portugal. To guarantee a high scientific standard, all the contributions were reviewed by at least three researchers, and only 20 papers were accepted and included in these proceedings. The papers are organized into sections on constraints, search, knowledge representation, temporal reasoning, planning, diagnosis and repair, and learning.