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• catalog abstract "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.".
• catalog contributor b13119948.
• catalog contributor b13119949.
• catalog contributor b13119950.
• catalog created "2003.".
• catalog date "2003".
• catalog date "2003.".
• catalog dateCopyrighted "2003.".
• catalog description "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.".
• catalog description "Keynote Papers -- Ontology -- Problem Solving -- Knowledge Discovery and Data Mining I -- Knowledge Discovery and Data Milling II -- Expert Systems -- Neural Networks Applications -- Belief Revisioii and Theorem Proving -- Reasoning and Logic -- Machine Learning I -- AI Applications -- Neural Networks -- Intelligent Agents -- Computer Vision -- AI & Medical Applications -- Machine Learning II -- Machilie Learning and Language -- Artificial Intelligence I -- AI \& Business -- Soft Computing -- Language Understanding -- Theory -- Artificial Intelligence II.".
• catalog extent "xvi, 1075 p. :".
• catalog identifier "3540206469 (softcover : alk. paper)".
• catalog isPartOf "Lecture notes in computer science ; 2903.".
• catalog isPartOf "Lecture notes in computer science, 0302-9743 ; 2903. Lecture notes in artificial intelligence".
• catalog isPartOf "Lecture notes in computer science. Lecture notes in artificial intelligence.".
• catalog issued "2003".
• catalog issued "2003.".
• catalog language "eng".
• catalog publisher "Berlin ; New York : Springer,".
• catalog subject "006.3 22".
• catalog subject "Artificial intelligence Congresses.".
• catalog subject "Artificial intelligence.".
• catalog subject "Computer science.".
• catalog subject "Database management.".
• catalog subject "Information storage and retrieval systems.".
• catalog subject "Information systems.".
• catalog subject "Q334 .A43 2003".
• catalog tableOfContents "Keynote Papers -- Ontology -- Problem Solving -- Knowledge Discovery and Data Mining I -- Knowledge Discovery and Data Milling II -- Expert Systems -- Neural Networks Applications -- Belief Revisioii and Theorem Proving -- Reasoning and Logic -- Machine Learning I -- AI Applications -- Neural Networks -- Intelligent Agents -- Computer Vision -- AI & Medical Applications -- Machine Learning II -- Machilie Learning and Language -- Artificial Intelligence I -- AI \& Business -- Soft Computing -- Language Understanding -- Theory -- Artificial Intelligence II.".
• catalog title "AI 2003 : advances in artificial intelligence : 16th Australian Conference on AI, Perth, Australia, December 3-5, 2003 : proceedings / Tamás D. Gedeon, Lance Chun Che Fung (eds).".
• catalog type "text".