AI 2003: Advances in Artificial Intelligence (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
1078
Utgivningsdatum
2003-11-01
Upplaga
2003 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Gedeon, Tamas D. (ed.), Fung, Lance Chun Che (ed.)
Illustratör/Fotograf
Bibliographie
Illustrationer
XXXII, 1078 p.
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540206460
AI 2003: Advances in Artificial Intelligence (häftad)

AI 2003: Advances in Artificial Intelligence

16th Australian Conference on AI, Perth, Australia, December 3-5, 2003, Proceedings

Häftad Engelska, 2003-11-01
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Consider the problem of a robot (algorithm, learning mechanism) moving along the real line attempting to locate a particular point ? . To assist the me- anism, we assume that it can communicate with an Environment ("Oracle") which guides it with information regarding the direction in which it should go. If the Environment is deterministic the problem is the "Deterministic Point - cation Problem" which has been studied rather thoroughly [1]. In its pioneering version [1] the problem was presented in the setting that the Environment could charge the robot a cost which was proportional to the distance it was from the point sought for. The question of having multiple communicating robots locate a point on the line has also been studied [1, 2]. In the stochastic version of this problem, we consider the scenario when the learning mechanism attempts to locate a point in an interval with stochastic (i. e. , possibly erroneous) instead of deterministic responses from the environment. Thus when it should really be moving to the "right" it may be advised to move to the "left" and vice versa. Apart from the problem being of importance in its own right, the stoch- tic pointlocationproblemalsohas potentialapplications insolvingoptimization problems. Inmanyoptimizationsolutions-forexampleinimageprocessing,p- tern recognition and neural computing [5, 9, 11, 12, 14, 16, 19], the algorithm worksits wayfromits currentsolutionto the optimalsolutionbasedoninfor- tion that it currentlyhas. A crucialquestionis oneof determining the parameter whichtheoptimizationalgorithmshoulduse.
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Innehållsförteckning

Keynote Papers.- Discovery of Emerging Patterns and Their Use in Classification.- Robot Soccer: Science or Just Fun and Games?.- On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity.- Multimedia Analysis and Synthesis.- Ontology.- Modelling Message Handling System.- A New Approach for Concept-Based Web Search.- Representing the Spatial Relations in the Semantic Web Ontologies.- Inductive Construction of Ontologies from Formal Concept Analysis.- Problem Solving.- Dynamic Variable Filtering for Hard Random 3-SAT Problems.- A Proposal of an Efficient Crossover Using Fitness Prediction and Its Application.- A New Hybrid Genetic Algorithm for the Robust Graph Coloring Problem.- Estimating Problem Metrics for SAT Clause Weighting Local Search.- Knowledge Discovery and Data Mining I.- Information Extraction via Path Merging.- Natural Language Agreement Description for Reversible Grammars.- Token Identification Using HMM and PPM Models.- Korean Compound Noun Term Analysis Based on a Chart Parsing Technique.- Knowledge Discovery and Data Milling II.- A Language Modeling Approach to Search Distributed Text Databases.- Combining Multiple Host-Based Detectors Using Decision Tree.- Association Rule Discovery with Unbalanced Class Distributions.- Efficiently Mining Frequent Patterns from Dense Datasets Using a Cluster of Computers.- Expert Systems.- Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR.- Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule.- MML Inference of Decision Graphs with Multi-way Joins and Dynamic Attributes.- Selection of Parameters in Building Fuzzy Decision Trees.- Neural Networks Applications.- Tool Condition Monitoring in Drilling Using Artificial Neural Networks.- Software Verification of Redundancy in Neuro-Evolutionary Robotics.- A Firearm Identification System Based on Neural Network.- Predicting the Australian Stock Market Index Using Neural Networks Exploiting Dynamical Swings and Intermarket Influences.- Belief Revisioii and Theorem Proving.- A Tableaux System for Deontic Interpreted Systems.- Decidability of Propositionally Quantified Logics of Knowledge.- Some Logics of Belief and Disbelief.- Axiomatic Analysis of Negotiation Protocols.- Reasoning and Logic.- A Probabilistic Line Breaking Algorithm.- Semiring-Valued Satisfiability.- A Defeasible Logic of Policy-Based Intention.- Dynamic Agent Ordering in Distributed Constraint Satisfaction Problems.- Machine Learning I.- On Why Discretization Works for Naive-Bayes Classifiers.- Adjusting Dependence Relations for Semi-Lazy TAN Classifiers.- Reduction of Non Deterministic Automata for Hidden Markov Model Based Pattern Recognition Applications.- Unsupervised Learning of Correlated Multivariate Gaussian Mixture Models Using MML.- AI Applications.- Cooperative Learning in Self-Organizing E-Learner Communities Based on a Multi-Agents Mechanism.- The Effects of Material, Tempo and Search Depth on Win-Loss Ratios in Chess.- Using Multiple Classification Ripple Down Rules for Intelligent Tutoring System's Knowledge Acquisition.- Model-Based Reinforcement Learning for Alternating Markov Games.- Neural Networks.- HLabelSOM: Automatic Labelling of Self Organising Maps toward Hierarchical Visualisation for Information Retrieval.- Using Images to Compare Two Constructive Network Techniques.- Pareto Neuro-Ensembles.- Predicting the Distribution of Discrete Spatial Events Using Artificial Neural Networks.- Intelligent Agents.- Learning Action Selection Network of Intelligent Agent.- A Dynamic Self-Organizing E-Learner Communities with Improved Multi-agent Matchmaking Algorithm.- Learning to Survive: Increased Learning Rates by Communication in a Multi-agent System.- An Infrastructure for Agent Collaboration in Open Environments.- Computer Vision.- Fingerprint Images Segmentation Using Two Stages Coarse to Fine Discrimination Technique.- Automatic Fingerprint Center Point Determinatio