Results 1  10
of
307
Adaptive Constraint Satisfaction
 WORKSHOP OF THE UK PLANNING AND SCHEDULING
, 1996
"... Many different approaches have been applied to constraint satisfaction. These range from complete backtracking algorithms to sophisticated distributed configurations. However, most research effort in the field of constraint satisfaction algorithms has concentrated on the use of a single algorithm fo ..."
Abstract

Cited by 811 (43 self)
 Add to MetaCart
Many different approaches have been applied to constraint satisfaction. These range from complete backtracking algorithms to sophisticated distributed configurations. However, most research effort in the field of constraint satisfaction algorithms has concentrated on the use of a single algorithm for solving all problems. At the same time, a consensus appears to have developed to the effect that it is unlikely that any single algorithm is always the best choice for all classes of problem. In this paper we argue that an adaptive approach should play an important part in constraint satisfaction. This approach relaxes the commitment to using a single algorithm once search commences. As a result, we claim that it is possible to undertake a more focused approach to problem solving, allowing for the correction of bad algorithm choices and for capitalising on opportunities for gain by dynamically changing to more suitable candidates.
The Computational Complexity of Propositional STRIPS Planning
 Artificial Intelligence
, 1994
"... I present several computational complexity results for propositional STRIPS planning, i.e., STRIPS planning restricted to ground formulas. Different planning problems can be defined by restricting the type of formulas, placing limits on the number of pre and postconditions, by restricting negation ..."
Abstract

Cited by 299 (3 self)
 Add to MetaCart
I present several computational complexity results for propositional STRIPS planning, i.e., STRIPS planning restricted to ground formulas. Different planning problems can be defined by restricting the type of formulas, placing limits on the number of pre and postconditions, by restricting negation in pre and postconditions, and by requiring optimal plans. For these types of restrictions, I show when planning is tractable (polynomial) and intractable (NPhard) . In general, it is PSPACEcomplete to determine if a given planning instance has any solutions. Extremely severe restrictions on both the operators and the formulas are required to guarantee polynomial time or even NPcompleteness. For example, when only ground literals are permitted, determining plan existence is PSPACEcomplete even if operators are limited to two preconditions and two postconditions. When definite Horn ground formulas are permitted, determining plan existence is PSPACEcomplete even if operators are limited t...
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
 IEEE Transactions on Knowledge and Data Engineering
, 1998
"... In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various applica ..."
Abstract

Cited by 270 (22 self)
 Add to MetaCart
In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Artificial Intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weakcommitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weakcommitment search algorithm ...
Algorithms for Distributed Constraint Satisfaction: A Review
 In CP
, 2000
"... . When multiple agents are in a shared environment, there usually exist constraints among the possible actions of these agents. A distributed constraint satisfaction problem (distributed CSP) is a problem to find a consistent combination of actions that satisfies these interagent constraints. Vario ..."
Abstract

Cited by 203 (7 self)
 Add to MetaCart
. When multiple agents are in a shared environment, there usually exist constraints among the possible actions of these agents. A distributed constraint satisfaction problem (distributed CSP) is a problem to find a consistent combination of actions that satisfies these interagent constraints. Various application problems in multiagent systems can be formalized as distributed CSPs. This paper gives an overview of the existing research on distributed CSPs. First, we briefly describe the problem formalization and algorithms of normal, centralized CSPs. Then, we show the problem formalization and several MAS application problems of distributed CSPs. Furthermore, we describe a series of algorithms for solving distributed CSPs, i.e., the asynchronous backtracking, the asynchronous weakcommitment search, the distributed breakout, and distributed consistency algorithms. Finally,we showtwo extensions of the basic problem formalization of distributed CSPs, i.e., handling multiple local variables, and dealing with overconstrained problems. Keywords: Constraint Satisfaction, Search, distributed AI 1.
Nogood Recording for Static and Dynamic Constraint Satisfaction Problems
 International Journal of Artificial Intelligence Tools
, 1993
"... Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problem (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, fo ..."
Abstract

Cited by 111 (5 self)
 Add to MetaCart
Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problem (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The problem we consider here is the solution maintenance problem in such a dynamic CSP (DCSP). We propose a new class of constraint recording algorithms called Nogood Recording that may be used for solving both static and dynamic CSPs. It offers an interesting compromise, polynomially bounded in space, between an ATMSlike approach and the usual static constraint satisfaction algorithms. 1 Introduction The constraint satisfaction problem (CSP) model is widely used to represent and solve various AI related problems and provides fundamental tools in areas such as truth...
Backtracking Algorithms for Disjunctions of Temporal Constraints
 Artificial Intelligence
, 1998
"... We extend the framework of simple temporal problems studied originally by Dechter, Meiri and Pearl to consider constraints of the form x1 \Gamma y1 r1 : : : xn \Gamma yn rn , where x1 : : : xn ; y1 : : : yn are variables ranging over the real numbers, r1 : : : rn are real constants, and n 1. W ..."
Abstract

Cited by 106 (2 self)
 Add to MetaCart
We extend the framework of simple temporal problems studied originally by Dechter, Meiri and Pearl to consider constraints of the form x1 \Gamma y1 r1 : : : xn \Gamma yn rn , where x1 : : : xn ; y1 : : : yn are variables ranging over the real numbers, r1 : : : rn are real constants, and n 1. We have implemented four progressively more efficient algorithms for the consistency checking problem for this class of temporal constraints. We have partially ordered those algorithms according to the number of visited search nodes and the number of performed consistency checks. Finally, we have carried out a series of experimental results on the location of the hard region. The results show that hard problems occur at a critical value of the ratio of disjunctions to variables. This value is between 6 and 7. Introduction Reasoning with temporal constraints has been a hot research topic for the last fifteen years. The importance of this problem has been demonstrated in many areas of artifici...
Practical Applications of Constraint Programming
 CONSTRAINTS
, 1996
"... Constraint programming is newly flowering in industry. Several companies have recently started up to exploit the technology, and the number of industrial applications is now growing very quickly. This survey will seek, by examples, ..."
Abstract

Cited by 105 (1 self)
 Add to MetaCart
Constraint programming is newly flowering in industry. Several companies have recently started up to exploit the technology, and the number of industrial applications is now growing very quickly. This survey will seek, by examples,
Genet: A connectionist architecture for solving constraint satisfaction problems by iterative improvement
 In Proceedings of AAAI'94
, 1994
"... New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima. In th ..."
Abstract

Cited by 94 (20 self)
 Add to MetaCart
New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima. In this paper we present genet, a connectionist architecture for solving binary and general constraint satisfaction problems by iterative improvement. genet incorporates a learning strategy to escape from local minima. Although genet has been designed to be implemented on vlsi hardware, we present empirical evidence to show that even when simulated on a single processor genet can outperform existing iterative improvement techniques on hard instances of certain constraint satisfaction problems.
Bridging the gap between planning and scheduling
 Knowledge Engineering Review
"... Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting orde ..."
Abstract

Cited by 94 (9 self)
 Add to MetaCart
Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems. 1 The Ambitious Spacecraft Imagine a hypothetical spacecraft enroute to a distant planet. Between propulsion cycles, there are time windows when the craft can be turned for communication and scientific observations. At any given time, the spacecraft has a large set of possible scientific observations that it can perform, each having some value or priority. For each observation, the spacecraft will need to be turned towards the target and the required measurement or exposure taken. Unfortunately, turning to a target is a slow operation that may take up to 30 minutes, depending on the magnitude of the turn. As a result, the choice of experiments and the order in which they are performed has a significant impact on the duration of turns and, therefore, on how much can be accomplished. All this is further complicated by several things:
Automatically configuring constraint satisfaction programs: A case study
 CONSTRAINTS
, 1996
"... Multitac is a learning system that synthesizes heuristic constraint satisfaction programs. The system takes a library of generic algorithms and heuristics and specializes them for a particular application. We present a detailed case study with three different distributions ofa single combinatorial ..."
Abstract

Cited by 91 (4 self)
 Add to MetaCart
Multitac is a learning system that synthesizes heuristic constraint satisfaction programs. The system takes a library of generic algorithms and heuristics and specializes them for a particular application. We present a detailed case study with three different distributions ofa single combinatorial problem, "Minimum Maximal Matching", and show that Multitac can synthesize programs for these different distributions that perform on par with handcoded programs and that exceed the performance of some wellknown satisfiability algorithms. In synthesizing a program, Multitac bases its choice of heuristics on an instance distribution, and we demonstrate that this capability has a significant impact on the results.