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19
Adaptive Constraint Satisfaction: The Quickest First Principle
- EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 1996
"... The choice of a particular algorithm for solving a given class of constraint satisfaction problems is often confused by exceptional behaviour of algorithms. One method of reducing the impact of this exceptional behaviour is to adopt an adaptive philosophy to constraint satisfaction problem solving. ..."
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Cited by 36 (3 self)
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The choice of a particular algorithm for solving a given class of constraint satisfaction problems is often confused by exceptional behaviour of algorithms. One method of reducing the impact of this exceptional behaviour is to adopt an adaptive philosophy to constraint satisfaction problem solving. In this report we describe one such adaptive algorithm, based on the principle of chaining. It is designed to avoid the phenomenon of exceptionally hard problem instances. Our algorithm shows how the speed of more naïve algorithms can be utilised safe in the knowledge that the exceptional behaviour can be bounded. Our work clearly demonstrates the potential benefits of the adaptive approach and opens a new front of research for the constraint satisfaction community.
Estimating Search Tree Size
- In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI ’06
, 2006
"... We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronologi-cal backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree will be simil ..."
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Cited by 26 (2 self)
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We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronologi-cal backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree will be similar to the part we have so far explored. We compare these methods against an old method due to Knuth based on random probing. We show that these methods can reliably estimate the size of search trees explored by both optimiza-tion and decision procedures. We also demonstrate that these methods for estimating search tree size can be used to select the algorithm likely to perform best on a particular problem instance.
Algorithm Selection for Combinatorial Search Problems: A Survey
, 2012
"... The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a prob ..."
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Cited by 20 (5 self)
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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.
Heuristic selection for stochastic search optimization: Modeling solution quality by extreme value theory
- In Proceedings of the 10th International Conference on Principles and Practice of Constraint Programming
, 2004
"... Abstract. The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling algorithms such as heuristic-biased stochastic sampling (HBSS) and value-biased stochastic sampling (VBSS), wh ..."
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Cited by 13 (5 self)
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Abstract. The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling algorithms such as heuristic-biased stochastic sampling (HBSS) and value-biased stochastic sampling (VBSS), wherein a heuristic is used to bias a stochastic policy for choosing among alternative branches in the search tree. One complication in getting the most out of algorithms like HBSS and VBSS in a given problem domain is the need to identify the most effective search heuristic. In many domains, the relative performance of various heuristics tends to vary across different problem instances and no single heuristic dominates. In such cases, the choice of any given heuristic will be limiting and it would be advantageous to gain the collective power of several heuristics. Toward this goal, this paper describes a framework for integrating multiple heuristics within a stochastic sampling search algorithm. In its essence, the framework uses online-generated statistical models of the search performance of different base heuristics to select which to employ on each subsequent iteration of the search. To estimate the solution quality distribution resulting from repeated application of a strong heuristic within a stochastic search, we propose the use of models from extreme value theory (EVT). Our EVT-motivated approach is validated on the NP-Hard problem of resource-constrained project scheduling with time windows (RCPSP/max). Using VBSS as a base stochastic sampling algorithm, the integrated use of a set of project scheduling heuristics is shown to be competitive with the current best known heuristic algorithm for RCPSP/max and in some cases even improves upon best known solutions to difficult benchmark instances. 1
A study of mechanisms for improving robotic group performance
- Artificial Intelligence
"... Many collaborative multi-robot application domains have limited areas of operation that cause spatial conflicts between robotic teammates. These spatial conflicts can cause the team’s productivity to drop with the addition of robots. This phenomenon is impacted by the coordination methods used by th ..."
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Cited by 9 (4 self)
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Many collaborative multi-robot application domains have limited areas of operation that cause spatial conflicts between robotic teammates. These spatial conflicts can cause the team’s productivity to drop with the addition of robots. This phenomenon is impacted by the coordination methods used by the team-members, as different coordination methods yield radically different productivity results. However, selecting the best coordination method to be used by teammates is a formidable task. This paper presents techniques for creating adaptive coordination methods to address this challenge. We first present a combined coordination cost measure, CCC, to quantify the cost of group interactions. Our measure is useful for facilitating comparison between coordination methods, even when multiple cost factors are considered. We consistently find that as CCC values grow, group productivity falls. Using the CCC, we create adaptive coordination techniques that are able to dynamically adjust the efforts spent on coordination to match the number of perceived coordination conflicts in a group. We present two adaptation heuristics that are completely distributed and require no communication between robots. Using these heuristics, robots independently estimate their combined coordination cost (CCC), adjust their coordination methods to minimize it, and increase group productivity. We use simulated robots to perform thousands of experiment trials to demonstrate the efficacy of our approach. We show that using adaptive coordination methods
Automatic Evaluation and Selection of Problem-Solving Methods: Theory and Experiments
- Journal of Experimental and Theoretical Artificial Intelligence
"... The choice of the right problem-solving method, from available methods, is a crucial skill for experts in many areas. We present a technique for automatic selection among methods based on analysis of their past performances. We formalize the statistical problem involved in choosing an e#cient met ..."
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Cited by 6 (1 self)
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The choice of the right problem-solving method, from available methods, is a crucial skill for experts in many areas. We present a technique for automatic selection among methods based on analysis of their past performances. We formalize the statistical problem involved in choosing an e#cient method, derive a solution to this problem, and describe a selection algorithm. The algorithm not only chooses among available methods, but also decides when to abandon the chosen method if it takes too much time. We then extend the basic statistical technique to account for problem sizes and similarity among problems.
An Overview of Learning in the Multi-TAC System
, 1995
"... this paper we give an overview of the Multi-TAC system focusing on its learning mechanisms. We present one of the areas of current research in the project, and summarize some empirical results comparing the programs written by Multi-TAC to those written by humans ..."
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Cited by 5 (0 self)
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this paper we give an overview of the Multi-TAC system focusing on its learning mechanisms. We present one of the areas of current research in the project, and summarize some empirical results comparing the programs written by Multi-TAC to those written by humans
The Fortune survey
- III. Cigarettes. Fortune
"... For many years, artificial intelligence research has beenfocusing on inventing new algorithms and approachesfor solving similar kinds of problem instances. In some scenarios, a new algorithm is clearly superior to previous approaches. In the majority of cases however, a new approach will improve ove ..."
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Cited by 2 (0 self)
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For many years, artificial intelligence research has beenfocusing on inventing new algorithms and approachesfor solving similar kinds of problem instances. In some scenarios, a new algorithm is clearly superior to previous approaches. In the majority of cases however, a new approach will improve over the current state of the art for only some problem instances. This may be because it employs a heuristic that fails for instances of a certain type or because it makes other assumptions about the instance or environment that are not satisfied in some cases. Selecting the most suitable algorithm for a particular problem instance aims to mitigate these problems and has the potential to sig-nificantly increase performance in practice. This is known as the algorithm selection problem. The algorithm selection problem has, in many forms and with different names, cropped up in many areas of artificial intelligence in the last few decades. Today there exists a large amount of literature on it. Most publications are concerned with new ways of tackling this problem and solving it effi-ciently in practice. Especially for combinatorial search prob-lems, the application of algorithm selection techniques has resulted in significant performance improvements that lever-age the diversity of systems and techniques developed in
Improving the Performance of Vector Hyper-heuristics through Local Search
"... Hyper-heuristics are methodologies that allow us to selectively apply the most suitable heuristic given the properties of the problem at hand. They can be applied in CSP in different ways, but one way which has received attention in recent years is variable ordering by using hyper-heuristics. To sel ..."
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Cited by 1 (0 self)
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Hyper-heuristics are methodologies that allow us to selectively apply the most suitable heuristic given the properties of the problem at hand. They can be applied in CSP in different ways, but one way which has received attention in recent years is variable ordering by using hyper-heuristics. To select the next variable, a set of heuristics exist and the hyper-heuristic decides, considering the features that describe the instance at hand, which heuristic is more suitable to be applied at the moment. This paper explores a hyperheuristic model for variable ordering within CSP based on vector hyper-heuristics. Each hyper-heuristic is represented as a set of vectors that maps instance features to heuristics. These vector hyper-heuristics are constructed by going into a local search method that modifies the hyper-heuristics. The results suggest that the approach is able to combine the strengths of different heuristics to perform well on a wide range of instances and compensate for their weaknesses on specific instances, resulting in an improvement in the performance of the search compared against the heuristics applied in isolation. Categories and Subject Descriptors
