Design-to-time is an approach to problem-solving in resource-constrained domains where: multiple solution methods are available for tasks, those solution methods make tradeoffs in solution quality versus time, and satisficing solutions are acceptable. Design-to-time involves designing a solution to a problem that uses all available resources to maximize the solution quality within the available time. This paper defines the design-to-time approach in detail, contrasting it to the anytime algorithm approach, and presents a heuristic algorithm for designto -time real-time scheduling. Our blackboard architecture that implements the design-to-time approach is discussed and an example problem and solution from the Distributed Vehicle Monitoring Testbed (DVMT) is described in detail. Experimental results, generated using a simulation, show the effects of various parameters on scheduler performance. Finally we discuss future research goals and plans. 1 This work was partly supported by the Of...

There are many formal approaches to specifying how the mental state of an agent entails that it perform particular actions. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task environment, domain, or society of which agents will be a part. This paper presents such a task environment-oriented modeling framework that can work hand-in-hand with more agent-centered approaches. Our approach features careful attention to the quantitative computational interrelationships between tasks, to what information is available (and when) to update an agent's mental state, and to the general structure of the task environment rather than single-instance examples. A task environment model can be used for both analysis and simulation, it avoids the methodologicalproblems of relying solely on single-instance examples, and provides concrete, meaningful characterizations with which ...

Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the activities of distributed intelligent agents appear in many domains: the control of distributed sensor networks; multi-agent scheduling of people and/or machines; distributed diagnosis of errors in local-area or telephone networks; concurrent engineering; `software agents' for information gathering. The design of coordination mechanisms for group...

. Adaptation in open, multi-agent information gathering systems is important for several reasons. These reasons include the inability to accurately predict future problem-solving workloads, future changes in existing information requests, future failures and additions of agents and data supply resources, and other future task environment characteristic changes that require system reorganization. We have developed a multi-agent distributed system infrastructure, Retsina (REusable Task Structure-based Intelligent Network Agents) that handles adaptation in an open Internet environment. Adaptation occurs both at the individual agent level as well as at the overall agent organization level. The Retsina system has three types of agents. Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating p...

There are many formal approaches to specifying how the mental state of an agent entails the particular actions it will perform. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task environment, domain, or society of which agents will be a part. This paper presents such a task environment-oriented modeling framework that can work hand-in-hand with more agent-centered approaches. Our approach features careful attention to the quantitative computational interrelationships between tasks, to what information is available (and when) to update an agent's mental state, and to the general structure of the task environment rather than single-instance examples. A task environment model can be used for both analysis and simulation, it avoids the methodological problems of relying solely on single-instance examples, and provides concrete, meaningful characterizations with which to sta...

This paper shows how the distributions of objective parameters such as "the number of VLM methods seen by the maximally loaded agent" (

This paper surveys recent research in deliberative real-time artificial intelligence (AI). Major areas of study have been anytime algorithms, approximate processing, and large system architectures. We describe several systems in each of these areas, focusing both on progress within the field, and the costs, benefits and interactions among different problem and algorithm complexity limitations used in the surveyed work. 1 This is an invited paper that will appear in the journal Real-Time Systems in 1994. This material is based upon work supported by the National Science Foundation under Grant No. IRI-9208920, NSF contract CDA 8922572, DARPA under ONR contract N00014-92-J-1698 and ONR contract N00014-92-J-1450. The content of the information does not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred. 1 Introduction Historically, the field of AI has not been particularly concerned with real-time performance. The problems ta...

viewpoints. It is a tool for building and testing computational theories of coordination. TÆMS is compatible with both formal computational agent-centered approaches and experimental approaches. The framework allows us to both mathematically analyze (when possible) and quantitatively simulate the behavior of multi-agent systems with respect to interesting characteristics of the computational task environments of which they are part. We believe that it provides the correct level of abstraction for meaningfully evaluating centralized, parallel, and distributed control algorithms, negotiation strategies, and organizational designs. This chapter will briefly describe the TÆMS modeling framework for representing abstract task environments, concentrating particularly on its support for simulation. I will describe how to model each of several different multi-agent problem-solving environments, such as This work was supported by DARPA contract N0

One of the most important uses of agent models is for problem-solving coordination. Coordination has been defined as managing the interdependencies between activities, or pragmatically as choosing, ordering, and locating actions in time in an attempt to maximize a possibly changing set of decision criteria. Coordination activities include not only localized agent interactions over specific problems, but also longerterm agent organizations that can support current and future problem-solving activity. Models that support coordination can be divided into three classes: models of other agents' current intended actions, schedules, and/or plans; models of other agents' objectives (desires, goals); and models of other agents' capabilities. This paper will focus on the longer-term models of capabilities that agents need in order to organize effectively to solve problems. Such models deal with an agent's capabilities and long-term commitments to certain classes of actions. Besides discussing th...

this paper, we present a learning algorithm that endows agents with the capability to choose a suitable subset of the coordination mechanisms based on the present problem solving situation. The rest of the paper is organized as follows. Section 2 discusses the coordination mechanisms in GPGP. Section 3 introduces our algorithm for situation-specific coordination. We then present a few early experiments in Section 4 and conclude. 2 Coordination in GPGP