Multi-tac 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 Multi-tac can synthesize programs for these different distributions that perform on par with hand-coded programs and that exceed the performance of some well-known satisfiability algorithms. In synthesizing a program, Multi-tac bases its choice of heuristics on an instance distribution, and we demonstrate that this capability has a significant impact on the results.

Conflict resolution is a critical problem in distributed and collaborative multi-agent systems. Negotiation via argumentation (NVA), where agents provide explicit arguments or justifications for their proposals for resolving conflicts, is an effective approach to resolve conflicts. Indeed, we are applying argumentation in some realworld multi-agent applications. However, a key problem in such applications is that a well-understood computational model of argumentation is currently missing, making it difficult to investigate convergence and scalability of argumentation techniques, and to understand and characterize different collaborative NVA strategies in a principled manner. To alleviate these difficulties, we present distributed constraint satisfaction problem (DCSP) as a computational model for investigating NVA. We model argumentation as constraint propagation in DCSP. This model enables us to study convergence properties of argumentation, and formulate and experimentally compare 16 different NVA strategies with different levels of agent cooperativeness towards others. One surprising result from our experiments is that maximizing cooperativeness is not necessarily the best strategy even in a completely cooperative environment. The paper illustrates the usefulness of these results in applying NVA to multi-agent systems, as well as to DCSP systems in general.

This paper provides a comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms. We review approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies. Besides describing briefly each of these approaches (or groups of techniques), we provide some criticism regarding their highlights and drawbacks. A small comparative study is also conducted, in order to assess the performance of several penalty-based approaches with respect to a dominance-based technique proposed by the author, and with respect to some mathematical programming approaches. Finally, we provide some guidelines regarding how to select the most appropriate constraint-handling technique for a certain application, ad we conclude with some of the the most promising paths of future research in this area.

This paper describes our experience with a simple modeling and programming approach for increasing the amount of constraint propagation in the constraint solving process. The idea, although similar to redundant constraints, is based on the concept of redundant modeling. We introduce the notions of CSP model and model redundancy, and show how mutually redundant models can be combined and connected using channeling constraints. The combined model contains the mutually redundant models as sub-models. Channeling constraints allow the sub-models to cooperate during constraint solving by propagating constraints freely amongst the sub-models. This extra level of pruning and propagation activities becomes the source of execution speedup. We perform two case studies to evaluate the effectiveness and efficiency of our method. The first case study is based on the simple and well-known n-queens problem, while the second case study applies our method in the design and construction of a real-life ...

Terminological systems, such as KL-ONE and K-Rep, are widely used in AI to represent and reason with concept descriptions. They compute subsumption relations between concepts and automatically classify concepts into a taxonomy. Each concept in the taxonomy describes a set of possible instances which are a superset of those described by its descendants. One limitation of current systems is their inability to handle complex compositions of concepts, such as constraint networks where each node is described by an associated concept. For example, plans are often represented (in part) as collections of actions related by a rich variety of temporal constraints. The T-REX system integrates terminological reasoning with constraint network reasoning to classify such plans, producing a "terminological" plan library. T-REX also introduces a new view of plan recognition as a process which dynamically partitions the plan library by modalities, e.g., necessary, possible and impo...

Enforcing local consistencies is one of the main features of constraint reasoning. Which level of local consistency should be used when searching for solutions in a constraint network is a basic question. Arc consistency and partial forms of arc consistency have been widely studied, and have been known for sometime through the forward checking or the MAC search algorithms. Until recently, stronger forms of local consistency remained limited to those that change the structure of the constraint graph, and thus, could not be used in practice, especially on large networks. This paper focuses on the local consistencies that are stronger than arc consistency, without changing the structure of the network, i.e., only removing inconsistent values from the domains. In the last five years, several such local consistencies have been proposed by us or by others. We make an overview of all of them, and highlight some relations between them. We compare them both theoretically and experimentally, considering their pruning efficiency and the time required to enforce them.

... this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system -- the Adaptive Place Advisor -- treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next, presenting complete items to the user only when a few remain. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item

The Constraint Satisfaction Problem is a type of combinatorial search problem of much interest in Artificial Intelligence and Operations Research. The simplest algorithm for solving such a problem is chronological backtracking, but this method suffers from a malady known as "thrashing," in which essentially the same subproblems end up being solved repeatedly. Intelligent backtracking algorithms, such as backjumping and dependency-directed backtracking, were designed to address this difficulty, but the exact utility and range of applicability of these techniques have not been fully explored. This dissertation describes an experimental and theoretical investigation into the power of these intelligent backtracking algorithms. We compare the empirical performance of several such algorithms on a range of problem distributions. We show that the more sophisticated algorithms are especially useful on those problems with a small number of constraints that happen to be difficult for chronologica...

Many practical problems in Artificial Intelligence have search trees that are too large to search exhaustively in the amount of time allowed. Systematic techniques such as chronological backtracking can be applied to these problems, but the order in which they examine nodes makes them unlikely to find a solution in the explored fraction of the space. Nonsystematic techniques have been proposed to alleviate the problem by searching nodes in a random order. A technique known as iterative sampling follows random paths from the root of the tree to the fringe, stopping if a path ends at a goal node. Although the nonsystematic techniques do not suffer from the problem of exploring nodes in a bad order, they do reconsider nodes they have already ruled out, a problem that is serious when the density of solutions in the tree is low. Unfortunately, for many practical problems the order of examing nodes matters and the density of solutions is low. Consequently, neither chronological backtracking...

In this paper we develop a representation for the temporal structure inherent in human actions and demonstrate an effective method for using that representation to detect the occurrence of actions. The temporal structure of the action, sub-actions, events, and sensor information is described using a constraint network based on Allen's interval algebra. We map these networks onto a simpler, 3-valued domain (past,now,fut) network --- a PNF-network --- to allow fast detection of actions and sub-actions. The occurrence of an action is computed by considering the minimal domain of its PNF-network, under constraints imposed by the current state of the sensors and the previous states of the network. We illustrate the approach with examples, showing that a major advantage of PNF propagation is the detection and removal of situations inconsistent with the temporal structure of the action. We also examine a method to increase the robustness of PNF-propagation in the case of faulty sensors. 1 In...