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Heuristic Search

Heuristic Search
  • Author : Stefan Edelkamp,Stefan Schroedl
  • Publisher :Unknown
  • Release Date :2011-05-31
  • Total pages :712
  • ISBN : 0080919731
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Summary : Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us. Provides real-world success stories and case studies for heuristic search algorithms Includes many AI developments not yet covered in textbooks such as pattern databases, symbolic search, and parallel processing units

Multiobjective Heuristic Search

Multiobjective Heuristic Search
  • Author : Pallab Dasgupta,P. P. Chakrabarti,S. C. DeSarkar
  • Publisher :Unknown
  • Release Date :2013-11-11
  • Total pages :134
  • ISBN : 9783322868534
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Summary : Solutions to most real-world optimization problems involve a trade-off between multiple conflicting and non-commensurate objectives. Some of the most challenging ones are area-delay trade-off in VLSI synthesis and design space exploration, time-space trade-off in computation, and multi-strategy games. Conventional search techniques are not equipped to handle the partial order state spaces of multiobjective problems since they inherently assume a single scalar objective function. Multiobjective heuristic search techniques have been developed to specifically address multicriteria combinatorial optimization problems. This text describes the multiobjective search model and develops the theoretical foundations of the subject, including complexity results . The fundamental algorithms for three major problem formulation schemes, namely state-space formulations, problem-reduction formulations, and game-tree formulations are developed with the support of illustrative examples. Applications of multiobjective search techniques to synthesis problems in VLSI, and operations research are considered. This text provides a complete picture on contemporary research on multiobjective search, most of which is the contribution of the authors.

Modern Heuristic Search Methods

Modern Heuristic Search Methods
  • Author : V. J. Rayward-Smith,I. H. Osman,C. R. Reeves,G. D. Smith
  • Publisher :Unknown
  • Release Date :1996-12-23
  • Total pages :294
  • ISBN : UOM:39015040654199
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Summary : Including contributions from leading experts in the field, this book covers applications and developments of heuristic search methods for solving complex optimization problems. The book covers various local search strategies including genetic algorithms, simulated annealing, tabu search and hybrids thereof. These methods have proved extraordinarily successful by solving some of the most difficult, real-world problems. At the interface between Artificial Intelligence and Operational Research, research in this exciting area is progressing apace spurred on by the needs of industry and commerce. The introductory chapter provides a clear overview of the basic techniques and useful pointers to further reading and to current research. The second section of the book covers some of the most recent and exciting developments of the basic techniques, with suggestions not only for extending and improving these but also for hybridizing and incorporating automatic adaption. The third section contains a number of case studies, surveys and comparative studies which span a wide range of application areas ranging from the classic Steiner tree problem to more practical problems arising in telecommunications and data analysis. The coverage of the latest research and the illustrative case studies will ensure that the book is invaluable for researchers and professionals with an interest in heuristic search methods.

Heuristic Search

Heuristic Search
  • Author : Saïd Salhi
  • Publisher :Unknown
  • Release Date :2017-02-18
  • Total pages :213
  • ISBN : 9783319493558
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Summary : This book aims to provide a general overview of heuristic search, to present the basic steps of the most popular heuristics, and to stress their hidden difficulties as well as their opportunities. It provides a comprehensive understanding of Heuristic search, the applications of which are now widely used in a variety of industries including engineering, finance, sport, management and medicine. It intends to aid researchers and practitioners in solving complex combinatorial and global optimisation problems, and spark interest in this exciting decision science-based subject. It will provide the reader with challenging and lively methodologies through which they will be able to design and analyse their own techniques

Heuristics

Heuristics
  • Author : Judea Pearl,dea Pearl
  • Publisher :Unknown
  • Release Date :1984
  • Total pages :382
  • ISBN : UOM:39015047795607
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Summary : Problem-solving strartegies and the nature of Heuristic informatio n.Heuristics and problem representations. Basic Heuristic-Search procedures. Formal properties of Heuristic methods. Heuristics viewed as information provided by simplified models. Performance analysis of Heuristic methods. Abstract models for quantitative performace analysis. Complexity versus precision of admissible Heuristics. Searching with nonadmissible Heuristics. Game-playing programs. Strategies and models for game-playing programs. Performace analysis for game-searching strategies. Decision quality in game searching. Bibliography. Index.

Heuristic Search Under Uncertainty

Heuristic Search Under Uncertainty
  • Author : Douglas Mayfield
  • Publisher :Unknown
  • Release Date :1993
  • Total pages :64
  • ISBN : UCAL:X53573
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Summary :

Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search

Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search
  • Author : David L. Woodruff
  • Publisher :Unknown
  • Release Date :2013-03-14
  • Total pages :312
  • ISBN : 9781475728071
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Summary : Computer Science and Operations Research continue to have a synergistic relationship and this book - as a part of the Operations Research and Computer Science Interface Series - sits squarely in the center of the confluence of these two technical research communities. The research presented in the volume is evidence of the expanding frontiers of these two intersecting disciplines and provides researchers and practitioners with new work in the areas of logic programming, stochastic optimization, heuristic search and post-solution analysis for integer programs. The chapter topics span the spectrum of application level. Some of the chapters are highly applied and others represent work in which the application potential is only beginning. In addition, each chapter contains expository material and reviews of the literature designed to enhance the participation of the reader in this expanding interface.

Bi-directional and Heuristic Search in Path Problems

Bi-directional and Heuristic Search in Path Problems
  • Author : Ira Pohl,Stanford University. Computer Science Department
  • Publisher :Unknown
  • Release Date :1969
  • Total pages :157
  • ISBN : STANFORD:36105025636387
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Summary :

Heuristic Search and Its Transit Applications

Heuristic Search and Its Transit Applications
  • Author : Ching-Fang Liaw
  • Publisher :Unknown
  • Release Date :1994
  • Total pages :229
  • ISBN : UOM:39015032537014
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Summary :

Am: an Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search

Am: an Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search
  • Author : Douglas B. Lenat,Stanford University. Computer Science Department
  • Publisher :Unknown
  • Release Date :1976
  • Total pages :352
  • ISBN : STANFORD:36105025666145
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Summary : A program called 'AM', is described which models one aspect of elementary mathematics research: developing new concepts under the guidance of a large body of heuristic rules. 'Mathematics' is considered as a type of intelligent behavior, not as a finished product. The local heuristics communicate via an agenda mechanism, a global list of tasks for the system to perform and reasons why each task is plausible. A single task might direct AM to define a new concept, or to explore some facet of an existing concept, or to examine some empirical data for regularities, etc. Repeatedly, the program selects from the agenda the task having the best supporting reasons, and then executes it. Each concept is an active, structured knowledge module. A hundred very incomplete modules are initially provided, each one corresponding to an elementary set-theoretic concept (e.g., union). This provides a definite but immense 'space' which AM begins to explore. AM extends its knowledge base, ultimately rediscovering hundreds of common concepts (e.g., numbers) and theorems (e.g., unique factorization). This approach to plausible inference contains great powers and great limitations.

An Expert System for Cell Edge Detection Using a Heuristic Search Method

An Expert System for Cell Edge Detection Using a Heuristic Search Method
  • Author : Loling Song
  • Publisher :Unknown
  • Release Date :1989
  • Total pages :298
  • ISBN : UCAL:X39604
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Summary :

Theory of Randomized Search Heuristics

Theory of Randomized Search Heuristics
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 9789814466875
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Summary :

Search in Artificial Intelligence

Search in Artificial Intelligence
  • Author : Leveen Kanal,Vipin Kumar
  • Publisher :Unknown
  • Release Date :2012-12-06
  • Total pages :482
  • ISBN : 9781461387886
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Summary : Search is an important component of problem solving in artificial intelligence (AI) and, more generally, in computer science, engineering and operations research. Combinatorial optimization, decision analysis, game playing, learning, planning, pattern recognition, robotics and theorem proving are some of the areas in which search algbrithms playa key role. Less than a decade ago the conventional wisdom in artificial intelligence was that the best search algorithms had already been invented and the likelihood of finding new results in this area was very small. Since then many new insights and results have been obtained. For example, new algorithms for state space, AND/OR graph, and game tree search were discovered. Articles on new theoretical developments and experimental results on backtracking, heuristic search and constraint propaga tion were published. The relationships among various search and combinatorial algorithms in AI, Operations Research, and other fields were clarified. This volume brings together some of this recent work in a manner designed to be accessible to students and professionals interested in these new insights and developments.

Front-to-end Bidirectional Heuristic Search

Front-to-end Bidirectional Heuristic Search
  • Author : Joseph Kelly Barker
  • Publisher :Unknown
  • Release Date :2015
  • Total pages :134
  • ISBN : OCLC:919218813
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Summary : Bidirectional heuristic search is a well-known technique for solving pathfinding problems. The goal in a pathfinding problem is to find paths---often of lowest cost---between nodes in a graph. Many real-world problems, such as finding the quickest route between two points in a map or measuring the similarity of DNA sequences, can be modeled as pathfinding problems. Bidirectional brute-force search does simultaneous brute-force searches forward from the initial state and backward from the goal states, finding solutions when both intersect. The idea of adding a heuristic to guide search is an old one, but has not seen widespread use and is generally believed to be ineffective. I present an intuitive explanation for the ineffectiveness of front-to-end bidirectional heuristic search. Previous work has examined this topic, but mine is the first comprehensive explanation for why most front-to-end bidirectional heuristic search algorithms will usually be outperformed by either unidirectional heuristic or bidirectional brute-force searches. However, I also provide a graph wherein bidirectional heuristic search does outperform both other approaches, as well as real-world problem instances from the road navigation domain. These demonstrate that there can be no general, formal proof of the technique's ineffectiveness. I tested my theory in a large number of popular search domains, confirming its predictions. One of my experiments demonstrates that a commonly-repeated explanation for the ineffectiveness of bidirectional heuristic search---that it spends most of its time proving solution optimality---is in fact wrong, and that with a strong heuristic a bidirectional heuristic search tends to find optimal solutions very late in a search. Finally, I introduce state-of-the-art solvers for the four-peg Towers of Hanoi with arbitrary initial and goal states, and peg solitaire, using disk-based, bidirectional algorithms. The Towers of Hanoi solver is a bidirectional brute-force solver which, as my theory predicts, outperforms a unidirectional heuristic solver. The peg solitaire solver is a bidirectional heuristic algorithm with novel heuristics. While my theory demonstrates that bidirectional heuristic search is generally ineffective, the peg solitaire domain demonstrates several caveats to my theory that this algorithm takes advantage of.

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
  • Author : Omid Bozorg-Haddad,Mohammad Solgi,Hugo A. Loáiciga
  • Publisher :Unknown
  • Release Date :2017-10-09
  • Total pages :304
  • ISBN : 9781119386995
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Summary : Overview of optimization -- Introduction to meta-heuristic and evolutionary algorithms -- Pattern search (PS) -- Genetic algorithm (GA) -- Simulated annealing (SA) -- Tabu search (TS) -- Ant colony optimization (ACO) -- Particle swarm optimization (PSO) -- Differential evolution (DE) -- Harmony search (HS) -- Shuffled frog-leaping algorithm (SFLA) -- Honey-bee mating optimization (HBMO) -- Invasive weed optimization (IWO) -- Central force optimization (CFO) -- Biogeography-based optimization (BBO) -- Firefly algorithm (FA) -- Gravity search algorithm (GSA) -- Bat algorithm (BA) -- Plant propagation algorithm (PPA) -- Water cycle algorithm (WCA) -- Symbiotic organisms search (SOS) -- Comprehensive evolutionary algorithm (CEA)

Artificial Intelligence

Artificial Intelligence
  • Author : David L. Poole,Alan K. Mackworth
  • Publisher :Unknown
  • Release Date :2017-09-25
  • Total pages :820
  • ISBN : 9781107195394
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Summary : Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Heuristic Search

Heuristic Search
  • Author : Saïd Salhi
  • Publisher :Unknown
  • Release Date :2018-07-25
  • Total pages :213
  • ISBN : 3319841432
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Summary : This book aims to provide a general overview of heuristic search, to present the basic steps of the most popular heuristics, and to stress their hidden difficulties as well as their opportunities. It provides a comprehensive understanding of Heuristic search, the applications of which are now widely used in a variety of industries including engineering, finance, sport, management and medicine. It intends to aid researchers and practitioners in solving complex combinatorial and global optimisation problems, and spark interest in this exciting decision science-based subject. It will provide the reader with challenging and lively methodologies through which they will be able to design and analyse their own techniques

A Theory of Heuristic Information in Game-Tree Search

A Theory of Heuristic Information in Game-Tree Search
  • Author : Chun-Hung Tzeng
  • Publisher :Unknown
  • Release Date :2012-12-06
  • Total pages :107
  • ISBN : 9783642613685
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Summary : Searching is an important process in most AI systems, especially in those AI production systems consisting of a global database, a set of production rules, and a control system. Because of the intractability of uninformed search procedures, the use of heuristic information is necessary in most searching processes of AI systems. This important concept of heuristic informatioD is the central topic of this book. We first use the 8-puzzle and the game tic-tac-toe (noughts and crosses) as examples to help our discussion. The 8-puzzle consists of eight numbered movable tiles set in a 3 x 3 frame. One cell of the frame is empty so that it is possible to move an adjacent numbered tile into the empty cell. Given two tile configurations, initial and goal, an 8-puzzle problem consists of changing the initial configuration into the goal configuration, as illustrated in Fig. 1.1. A solution to this problem is a sequence of moves leading from the initial configuration to the goal configuration, and an optimal solution is a solution having the smallest number of moves. Not all problems have solutions; for example, in Fig. 1.1, Problem 1 has many solutions while Problem 2 has no solution at all.

Encyclopedia of Systems Biology

Encyclopedia of Systems Biology
  • Author : Werner Dubitzky,Olaf Wolkenhauer,Hiroki Yokota,Kwang-Hyun Cho
  • Publisher :Unknown
  • Release Date :2013-08-17
  • Total pages :2367
  • ISBN : 1441998640
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Summary : Systems biology refers to the quantitative analysis of the dynamic interactions among several components of a biological system and aims to understand the behavior of the system as a whole. Systems biology involves the development and application of systems theory concepts for the study of complex biological systems through iteration over mathematical modeling, computational simulation and biological experimentation. Systems biology could be viewed as a tool to increase our understanding of biological systems, to develop more directed experiments, and to allow accurate predictions. The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology.

Speeding Up the Convergence of Online Heuristic Search and Scaling Up Offline Heuristic Search

Speeding Up the Convergence of Online Heuristic Search and Scaling Up Offline Heuristic Search
  • Author : David Andre Furcy
  • Publisher :Unknown
  • Release Date :2004
  • Total pages :229
  • ISBN : OCLC:57551331
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Summary : The most popular methods for solving the shortest-path problem in Artificial Intelligence are heuristic search algorithms. The main contributions of this research are new heuristic search algorithms that are either faster or scale up to larger problems than existing algorithms. Our contributions apply to both online and offline tasks. For online tasks, existing real-time heuristic search algorithms learn better informed heuristic values and in some cases eventually converge to a shortest path by repeatedly executing the action leading to a successor state with a minimum cost-to-goal estimate. In contrast, we claim that real-time heuristic search converges faster to a shortest path when it always selects an action leading to a state with a minimum f-value, where the f-value of a state is an estimate of the cost of a shortest path from start to goal via the state, just like in the offline A* search algorithm. We support this claim by implementing this new non-trivial action-selection rule in FALCONS and by showing empirically that FALCONS significantly reduces the number of actions to convergence of a state-of-the-art real-time search algorithm. For offline tasks, we improve on two existing ways of scaling up best-first search to larger problems. First, it is known that the WA* algorithm (a greedy variant of A*) solves larger problems when it is either diversified (i.e., when it performs expansions in parallel) or committed (i.e., when it chooses the state to expand next among a fixed-size subset of the set of generated but unexpanded states). We claim that WA* solves even larger problems when it is enhanced with both diversity and commitment. We support this claim with our MSC-KWA* algorithm. Second, it is known that breadth-first search solves larger problems when it prunes unpromising states, resulting in the beam search algorithm. We claim that beam search quickly solves even larger problems when it is enhanced with backtracking based on limited discrepancy search. We support this claim with our BULB algorithm. We show that both MSC-KWA* and BULB scale up to larger problems than several state-of-the-art offline search algorithms in three standard benchmark domains. Finally, we present an anytime variant of BULB and apply it to the multiple sequence alignment problem in biology.

AI 2003: Advances in Artificial Intelligence

AI 2003: Advances in Artificial Intelligence
  • Author : Tamas D. Gedeon,Lance C.C. Fung
  • Publisher :Unknown
  • Release Date :2003-11-24
  • Total pages :1075
  • ISBN : 9783540206460
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Summary : This book constitutes the refereed proceedings of the 16th Australian Conference on Artificial Intelligence, AI 2003, held in Perth, Australia in December 2003. The 87 revised full papers presented together with 4 keynote papers were carefully reviewed and selected from 179 submissions. The papers are organized in topical sections on ontologies, problem solving, knowledge discovery and data mining, expert systems, neural network applications, belief revision and theorem proving, reasoning and logic, machine learning, AI applications, neural computing, intelligent agents, computer vision, medical applications, machine learning and language, AI and business, soft computing, language understanding, and theory.