Satisficing and bounded optimality A position paper

AAAI Conferences

Each one of these problems can be approached and solved using optimizing or satisficing techniques. Each stage involves complex tradeoffs that can be addressed off-line or online. For example, using a more precise model of the environment may complicate the problem definition and may force the system to compute less precise answers to the problem. The key question is whether bounded optimality is a useful approach to all or some of these problems. In other words, the question is whether there are any advantages to making optimal decisions within an approximate model, rather than making approximate decisions within a more precise (or even perfect) model.


Metareasoning: A Manifesto

AAAI Conferences

This manifesto proposes a simple model of metareasoning that constitutes a general framework to organize research on this topic. The claim is that metareasoning, like the actionperception cycle of reasoning, is composed of the introspective monitoring of reasoning and the subsequent meta-level control of reasoning. This model holds for single agent and multiagent systems and is broad enough to include models of self. We offer the model as a short conversation piece to which the community can compare and contrast individual theories.


Using Anytime Algorithms in Intelligent Systems

AI Magazine

Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpretation, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems relate to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation. The term anytime algorithm was coined by Dean and Boddy in the mid-1980s in the context of their work on time-dependent planning (Dean and Boddy 1988; Dean 1987).


Using Anytime Algorithms in Intelligent Systems

AI Magazine

Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpretation, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems relate to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.


Operational Rationality through Compilation of Anytime Algorithms

AI Magazine

How can an artificial agent react to a situation after performing the correct amount of thinking? My Ph.D. dissertation (Zilberstein 1993)2 presents a theoretical framework and a programming paradigm that provide an answer to this question.