This paper provides an overview of the field of qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches. Though it inherits from a long tradition, the field offers a new focus on a number of important unanswered questions of common concern to artificial intelligence, economics, law, psychology, and management.

This article examines prospects for theories and methods of preferences, both in the specific sense of the preferences of the ideal rational agents considered in economics and decision theory and in the broader interplay between reasoning and rationality considered in philosophy, psychology, and artificial intelligence. Modern applications seek to employ preferences as means for specifying, designing, and controlling rational behaviors as well as descriptive means for understanding behaviors. We seek to understand the nature and representation of preferences by examining the roles, origins, meaning, structure, evolution, and application of preferences.

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Major military operations and other large-scale activities naturally involve competitions for resources that must be managed effectively to ensure overall success. Since plan revisions may change the resource demands of the activity, the relevant competitions for resources involve computational resources (time, database control, etc.) as well as the more obvious non-computational resources (fuel, aircraft, etc.). Ideally, these conflicting demands should be resolved rationally in the sense of decision theory and economics. We determine rational allocations over the full range of resources by using an artificial market economy (implemented as RECON, the Reasoning ECONomy) to determine prices or trading ratios among resources. We represent tasks and goals as resource-endowed consumers and computational methods, informational resources, and reasoning procedures as resource-transforming producers. We then use the information provided by such problem-oriented market economies to guide searc...

This paper provides the reader with a presentation of preference modelling fundamental notions as well as some recent results in this field. Preference modelling is an inevitable step in a variety of fields: economy, sociology, psychology, mathematical programming, even medicine, archaeology, and obviously decision analysis. Our notation and some basic definitions, such as those of binary relation, properties and ordered sets, are presented at the beginning of the paper. We start by discussing different reasons for constructing a model or preference. We then go through a number of issues that influence the construction of preference models. Different formalisations besides classical logic such as fuzzy sets and non-classical logics become necessary. We then present different types of preference structures reflecting the behavior of a decision-maker: classical, extended and valued ones. It is relevant to have a numerical representation of preferences: functional representations, value functions. The concepts of thresholds and minimal representation are also introduced in this section. In section 7, we briefly explore the concept of deontic logic (logic of preference) and other formalisms associated with "compact representation of preferences " introduced for special purposes. We end the paper with some concluding remarks.

Efficiency dictates that plans for large-scale distributed activities be revised incrementally, with parts of plans being revised only if the expected utility of identifying and revising the subplans improve on the expected utility of using the original plan. The problems of identifying and reconsidering the subplans affected by changed circumstances or goals are closely related to the problems of revising beliefs as new or changed information is gained. But traditional techniques of reason maintenance---the standard method for belief revision---choose revisions arbitrarily and enforce global notions of consistency and groundedness which may mean reconsidering all beliefs or plan elements at each step. We develop revision methods aiming to revise only those beliefs and plans worth revising, and to tolerate incoherence and ungroundedness when these are judged less detrimental than a costly revision effort. We use an artificial market economy in planning and revision tasks to arrive at o...

Contents 1 Executive Summary 3 2 Innovative claims 4 3 Technical plan 9 3.1 Technical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.1 A vision of the future . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.2 Building on the MAITA system . . . . . . . . . . . . . . . . . . . 11 3.1.3 Monitoring libraries . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.3.1 Signal transducers: . . . . . . . . . . . . . . . . . . . . . 14 3.1.3.2 Signal correlators and trend templates: . . . . . . . . . . 14 3.1.4 Situation-sensitive monitors . . . . . . . . . . . . . . . . . . . . . 16 3.1.4.1 Situational awareness: . . . . . . . . . . . . . . . . . . . 16 3.1.4.2 Situation-dependent behaviors: . . . . . . . . . . . . . . 17 3.1.5 A security monitoring desk . . . . . . . . . . . . . . . . . . . . . . 18 3.1.5.1 Control actions: . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.5.2 Display actions: . . . . . . . . . . . . . . . . . . . . . .

The mathematical concepts of modern axiomatic rational mechanics apply more broadly than generally recognized, in that formal mechanical concepts apply directly and with slight adaptations to certain psychological and economic systems as well the familiar physical applications. These nonphysical applications provide new means for characterizing realistic notions of economic rationality and limits on reasoning abilities, translate psychology and economics into studies of new types of mechanical materials, and open traditional informal philosophical conceptions of materialism and dualism to new avenues of mathematical and experimental investigation. This semi-formal article, which summarizes a detailed book-length formal treatment [13], briefly sketches modern mechanics, the adaptation of mechanical axioms to cover hybrid and discrete systems, an illustrative formalization of a representative rational psychological system from artificial intelligence, and the new perspective on psychology and economics,