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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 ..."
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Cited by 110 (5 self)
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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...
A Constraint Satisfaction Framework for Decision Under Uncertainty
 In Proc. of the 11th Int. Conf. on Uncertainty in Artificial Intelligence
, 1995
"... The Constraint Satisfaction Problem (CSP) framework offers a simple and sound basis for representing and solving simple decision problems, without uncertainty. This paper is devoted to an extension of the CSP framework enabling us to deal with some decisions problems under uncertainty. This extensio ..."
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Cited by 25 (1 self)
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The Constraint Satisfaction Problem (CSP) framework offers a simple and sound basis for representing and solving simple decision problems, without uncertainty. This paper is devoted to an extension of the CSP framework enabling us to deal with some decisions problems under uncertainty. This extension relies on a differentiation between the agentcontrollable decision variables and the uncontrollable parameters whose values depend on the occurrenceof uncertain events. The uncertainty on the values of the parameters is assumed to be given under the form of a probability distribution. Two algorithms are given, for computing respectively decisions solving the problem with a maximal probability, and conditional decisions mapping the largest possible amount of possible cases to actual decisions. 1 Introduction Decision making is primarily a matter of choosing between alternatives that most commonly are expressed implicitly. Thus solving a decision problem amounts to generate the option(s) t...
Exploiting System Structure in ModelBased Diagnosis of DiscreteEvent Systems
, 1996
"... We describe an implemented system for modelbased diagnosis of discreteevent systems, which are continuous systems governed by discrete controllers. Our approach contributes to modelbased diagnosis by taking advantage of the system structure (a directed graph depicting component interconnectivity) ..."
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Cited by 23 (6 self)
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We describe an implemented system for modelbased diagnosis of discreteevent systems, which are continuous systems governed by discrete controllers. Our approach contributes to modelbased diagnosis by taking advantage of the system structure (a directed graph depicting component interconnectivity) to efficiently diagnose dynamic systems, and by providing computational guarantees based on such structure. Specifically, we use a propositional temporal logic to describe discreteevent systems, but constrain the temporal sentences defining a system using the topology of its structure. We describe a computational machinery for computing and focusing consistencybased diagnoses based on a structured set of temporal sentences, the complexity of which is dependent on the topology of the given structure. We explain how to engineer the system structure to ensure that our algorithms are efficient. We also explain why our approach has proven effective for diagnosing discreteevent systems by iden...
A Framework for Dynamic Constraint Reasoning using Procedural Constraints
 Proceedings of the Euopean Conference on Artificial Intelligence
, 2000
"... Many complex realworld decision and control problems contain an underlying constraint reasoning problem. This is particularly evident in a recently developed approach to planning, where almost all planning decisions are represented by constrained variables. The result is that a significant part o ..."
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Cited by 20 (7 self)
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Many complex realworld decision and control problems contain an underlying constraint reasoning problem. This is particularly evident in a recently developed approach to planning, where almost all planning decisions are represented by constrained variables. The result is that a significant part of the planning problem is translated into a constraint network whose consistency determines the validity of the candidate plan. Since higherlevel choices about control actions can add or remove variables and constraints, the underlying constraint network is invariably highly dynamic. Real planning problems require arbitrary domaindependent constraints in the constraint network, in addition to giving rise to continuous variables. These requirements present a number of significant challenges for a constraint reasoning mechanism. In this paper, we introduce a general framework for handling dynamic constraint networks with realvalued variables, by using procedures to represent and eff...
A Reactive Constraint Logic Programming Scheme
, 1995
"... In this paper we present a constraint logic programming scheme for reactive systems. A formal framework is developed to define the scheme's operational model and to prove its completeness. A prototype implementation of a simplified version of this model is described and then evaluated on two applica ..."
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Cited by 19 (5 self)
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In this paper we present a constraint logic programming scheme for reactive systems. A formal framework is developed to define the scheme's operational model and to prove its completeness. A prototype implementation of a simplified version of this model is described and then evaluated on two applications.
A logical notion of conditional independence: Properties and applications
 ARTIFICIAL INTELLIGENCE
, 1997
"... We propose a notion of conditional independence with respect to propositional logic and study some of its key properties. We present several equivalent formulations of the proposed notion, each oriented towards a specific application of logical reasoning such as abduction and diagnosis. We suggest a ..."
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Cited by 17 (3 self)
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We propose a notion of conditional independence with respect to propositional logic and study some of its key properties. We present several equivalent formulations of the proposed notion, each oriented towards a specific application of logical reasoning such as abduction and diagnosis. We suggest a framework for utilizing logical independence computationally by structuring a propositional logic database around a directed acyclic graph. This structuring explicates many of the independences satisfied by the underlying database. Based on these structural independences, we develop an algorithm for a class of structured databases that is not necessarily Horn. The algorithm is linear in the size of a database structure and can be used for deciding entailment, computing abductions and diagnoses. The presented results are motivated by similar results in the literature on probabilistic and constraintbased reasoning.
Modelbased Diagnosis using Causal Networks
 In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI
, 1995
"... This paper rests on several contributions. First, we introduce the notion of a consequence, which is a boolean expression that characterizes consistencybased diagnoses. Second, we introduce a basic algorithm for computing consequences when the system description is structured using a causal network ..."
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Cited by 16 (6 self)
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This paper rests on several contributions. First, we introduce the notion of a consequence, which is a boolean expression that characterizes consistencybased diagnoses. Second, we introduce a basic algorithm for computing consequences when the system description is structured using a causal network. We show that if the causal network has no undirected cycles, then a consequence has a linear size and can be computed in linear time. Finally, we show that diagnoses characterized by a consequence and meeting some preference criterion can be extracted from the consequence in time linear in its size. A dual set of results is provided for abductive diagnosis. 1
Lifted Search Engines for Satisfiability
, 1999
"... There are several powerful solvers for satisfiability (SAT), such as wsat, DavisPutnam, and relsat. However, in practice, the SAT encodings often have so many clauses that we exceed physical memory resources on attempting to solve them. This excessive size often arises because conversion to SAT, ..."
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Cited by 16 (3 self)
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There are several powerful solvers for satisfiability (SAT), such as wsat, DavisPutnam, and relsat. However, in practice, the SAT encodings often have so many clauses that we exceed physical memory resources on attempting to solve them. This excessive size often arises because conversion to SAT, from a more natural encoding using quantifications over domains, requires expanding quantifiers. This suggests that we should "lift" successful SAT solvers. That is, adapt the solvers to use quantified clauses instead of ground clauses. However, it was generally believed that such lifted solvers would be impractical: Partially, because of the overhead of handling the predicates and quantifiers, and partially because lifting would not allow essential indexing and caching schemes. Here we show that, to the contrary, it is not only practical to handle quantified clauses directly, but that lifting can give exponential savings. We do this by identifying certain tasks that are central to...
A new approach to modeling and solving minimal perturbation problems
 In Recent Advances in Constraints
, 2004
"... Abstract. Formulation of many reallife problems evolves when the problem is being solved. For example, a change in the environment might appear after the initial problem specification and this change must be reflected in the solution. Such changes complicate usage of a traditionally static constrai ..."
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Cited by 14 (4 self)
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Abstract. Formulation of many reallife problems evolves when the problem is being solved. For example, a change in the environment might appear after the initial problem specification and this change must be reflected in the solution. Such changes complicate usage of a traditionally static constraint satisfaction technology that requires the problem to be fully specified before the solving process starts. In this paper, we propose a new formal description of changes in the problem formulation called a minimal perturbation problem. This description focuses on the modification of the solution after a change in the problem specification. We also describe a new branchandbound like algorithm for solving such type of problems.
Incremental Adaptation of Constraint Handling Rule Derivations
 Journal of Applied Arti Intelligence, Special Issue on Constraint Handling Rules
, 1997
"... . Constraint solving in dynamic environments requires an immediate adaptation of previously evaluated solutions of constraint satisfaction problems if these problems are changed. After a change, an adapted solution is preferred which is stable, i.e. as close as possible to the original solution. ..."
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Cited by 6 (2 self)
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. Constraint solving in dynamic environments requires an immediate adaptation of previously evaluated solutions of constraint satisfaction problems if these problems are changed. After a change, an adapted solution is preferred which is stable, i.e. as close as possible to the original solution. A wide range of incremental constraint solving methods for dynamic finite domain constraint satisfaction problems are known which satisfy more or less this additional requirement. Fruhwirth's Constraint Handling Rules (CHRs) are a highlevel language extension to write constraint solvers. Up to now, more than 20 solvers for different kinds of constraints are available, including finitedomain constraints, linear equations and inequations, and set constraints. In this paper, a new general constraint solving method for dynamic constraint satisfaction problems is presented. In detail, a new incremental algorithm is presented which adapts CHR derivations after changes of the initial c...