Applied Intelligence 14, 235–236, 2001c© 2001 Kluwer Academic Publishers. Manufactured in The Netherlands.

Special Issue on Decision Theory and Artificial Intelligence

The AI community has paid more and more attention to decision theory since the early 90’s, for three mainreasons:

1. some subareas of AI are concerned with decision making (typically, planning, but also scheduling, diagnosisand repair, multiagent communication and cooperation, reinforcement learning, games), therefore they can berevisited using decision-theoretic models;

2. AI traditionally focuses on representation languages (for knowledge, but also for preferences or goals) whiledecision theory traditionally focuses on axiomatic characterizations of rational decision making; thus AI canbring languages for knowledge and preference representation so a to express decision making problems;

3. AI has contributed to develop alternative models for uncertainty and preference (especially, qualitative models)that are of interest for building alternative approaches to classical decision theory.

The paper by Bonet and Geffner is concerned by both types of interaction 1 and 2. They give a synthetic overviewof models, languages and algorithms (together with experimental results) for a variety of planning problems obtainedby letting the nature of uncertainty and observability involved in the problem vary.

The next three papers are concerned with the first type of interaction.Voorbraak and Massios consider a specific case of planning under incomplete knowledge and partial observability,

namely, when the actions consist of surveillance tasks (inspecting a given location by means of a sensor). Theplanning problem considered by Alechina and Logan does not involve any uncertainty but several criteria: theauthors propose a new search algorithm for state space search under constraints (and multiple criteria), that theyapply to a route planning problem.

While planning has a lot to gain in using models from decision under uncertainty (see the paper by Voorbraak andMassios and as well the abovementioned paper by Bonet and Geffner) and multicriteria decision making (see thepaper by Alechina and Logan), multiagent communication and cooperation has much to gain by using models fromgame theory. The paper by Golfarelli and Meuleau is about decision under incomplete knowledge in a two-persongame-theoretical framework: uncertainty comes from the other player’s move (while in the papers by Bonet andGeffner and by Voorbraak and Massios, it comes from nature). It provides a meaningful transformation of a two-player, one-move alternating game with partially observable knowledge into a simultaneous fully observable game,modulo an change of the structure. This is the only paper of the special issue where several agents are involved—allother papers are concerned with individual decision making.

The next three papers are concerned with qualitative models and logical representation languages for preferences.Van der Torre and Weydert develop a language for representing preferences which shares many features with

nonmonotonic logics—note that the latter have been traditionally developed for representing knowledge rather thanpreference. A similar remark applies to the paper by Benferhat, Dubois and Prade, and the paper by Godo andZapico, which both show how possibilistic logic (that was initially developed for managing uncertainty) can beused for representing preferences. Another common point of these three papers is that they make use of semi-qualitative representations of preference. Van der Torre and Weydert develop a very modular language where afamily of (numerical) utility functions is induced from a semi-qualitative, semi-numerical input. Benferhat, Duboisand Prade give two methods (respectively, goal-based and constraint-based) of generating preference relations inthe possibilistic logic setting. In the same possibilistic setting, Godo and Zapico show how to deal with partiallyinconsistent belief states.

236 Lang

Jerome Lang is a CNRS full-time researcher at the Institut de Recherche en Informatique de Toulouse (France). He obtained his PhD inComputer Science from the Universite Paul Sabatier of Toulouse in 1991. His research is mainly concerned by the study of logical representationlanguages and computational issues (complexity and algorithms) for reasoning and decision making under uncertainty.

Jerome LangInstitut de Recherche en Informatique de Toulouse University,Paul Sabatier, Toulouse Cedex, 31062, Francelang@irit.fr