5609948
Reflections on the Nature of Multi-Agent Coordination and Its Implications for an Agent Architecture
The development of enabling infrastructure for the next generation of multi-agent systems consisting of large numbers of agents and operating in open environments is one of the key challenges for the multi-agent community.Current infrastructure support does not materially assist in the development of sophisticated agent coordination strategies. It is the need for and the development of such a high-level support structure that will be the focus of this paper. A domain-independent (generic) agent architecture is proposed that wraps around an agent's problem-solving component in order to make problem solving responsive to real-time constraints, available network resources, and the need to coordinate—both in the large and small—with problem-solving activities of other agents. This architecture contains five components, local agent scheduling, multi-agent coordination, organizational design, detection and diagnosis, and on-line learning, that are designed to interact so that a range of different situation-specific coordination strategies can be implemented and adapted as the situation evolves. The presentation of this architecture is followed by a more detailed discussion on the interaction among these components and the research questions that need to be answered to understand the appropriateness of this architecture for the next generation of multi-agent systems.
6313593
Solving distributed constraint optimization problems using cooperative mediation
Distributed Constraint Optimization Problems (DCDP) have, for a long time, been considered an important research area for multi-agent systems because a vast number of real-world situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve them efficiently using fully distributed algorithms which are often based on existing centralized techniques. In this paper, we present an optimal, distributed algorithm called optimal asynchronous partial overlay (OptAPO) for solving DCOPs that is based on a partial centralization technique called cooperative mediation. The key ideas used by this algorithm are that agents, when acting as a mediator, centralize relevant portions of the DCDP, that these centralized subproblems overlap, and that agents increase the size of their subproblems as the problem solving unfolds. We present empirical evidence that shows that OptAPO performs better than other known, optimal DCOP techniques.
7638865
A Cooperative mediation-based protocol for dynamic distributed resource allocation
In this paper, we present a cooperative mediation-based protocol that solves a distributed resource allocation problem while conforming to soft real-time constraints in a dynamic environment. Two central principles are used in this protocol that allow it to operate in constantly changing conditions. First, we frame the allocation problem as an optimization problem, similar to a partial constraint satisfaction problem (PCSP), and use relaxation techniques to derive conflict (constraint violation) free solutions. Second, by using overlapping mediation sessions to conduct the search, we are able to prune large parts of the search space by using a form of arc-consistency. This allows the protocol to both quickly identify situations when the problem is over-constrained, and to determine the appropriate repair. From the global perspective, the protocol has a hill climbing behavior and because it was designed to work in dynamic environments, is approximate. We describe the domain which inspired the creation of this protocol, as well as discuss experimental results
8682991
A New Framework for Sensor Interpretation: Planning to Resolve Sources of Uncertainty
Sensor interpret.ation involves the determination of high-level explalliations of sensor data. Blackboard-based interpretation systems have usually been limited to incremental hypothesize and test strategies for resolving uncertainty. We have developed a new interpretation framework that supports the use of more sophisticated strat1egies like differential diagnosis. The RESUN framework has two key components: an evidential represent,ation that includes explicit, symbolic encodings of the sources of uncertainty (SOUs) in the evidence for hypotheses and a script-based, incremental control planner. Interpretation is viewed as an incremental process of gathering evidence to resolve particular sources of uncertainty. Control plans invoke actions that examine the symbolic SOUs associated with hypotheses and use the resulting information to post goals to resolve uncertadnty. These goals direct the system to expand methods appropriate for resolving the current sources of uncertlLinty in the hypotheses. The planner's refocusing mechanism makes it possible to postpone focusing decisions when there is insufficient information to make decisions and provides opportunistic control capabilities. The RESUN framework has been implemented and experimentally verified using a simulated aircraft monitorilllg application.
3393161
BIG: An agent for resource-bounded information gathering and decision making
Abstract The World Wide Web has become an invaluable information resource but the explosion of available information has made Web search a time consuming and complex process. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering control problem. This paper describes the rationale, architecture, and implementation of a next generation information gathering system—a system that integrates several areas of Artificial Intelligence research under a single umbrella. Our solution to the information explosion is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource trade-offs of different possible gathering approaches, extracts information from both unstructured and structured documents, and uses the extracted information to refine its search and processing activities.
3446035
Resource-Bounded Searches in an Information Marketplace
Over the past five years, we have been developing a Web-based, resource-bounded, information-gathering agent-called BIG, for Bounded Information Gathering-to support a human decision process. Although its techniques are general enough to apply to a wide range of domains, BIG specifically helps clients pick software packages. For example, a client can instruct BIG to recommend a database package for Windows 98 and specify constraints on both the search process and the product characteristics. BIG will then formulate a plan, locate and extract relevant information from both structured and unstructured documents, and return a recommendation to the client, along with supporting data. Our focus is on a recently developed capability, namely, to schedule information-gathering activity in a way that controls the money spent on acquiring information from sites that charge a fee for access. This capability supports an information marketplace on the Web, which we feel will eventually supersede (but obviously not completely replace) the current model of free information access supported by advertising fees.
1194012
BIG: A Resource-Bounded Information Gathering Agent
Effective information gathering on the WWW is a complex task requiring planning, scheduling, text processing, and interpretation-style reasoning about extracted data to resolve inconsistencies and to refine hypotheses about the data. This paper describes the rationale, architecture, and implementation of a next generation information gathering system - a system that integrates several areas of AI research under a single research umbrella. The goal of this system is to exploit the vast number of information sources available today on the NII including a growing number of digital libraries, independent news agencies, government agencies, as well as human experts providing a variety of services. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering coordination problem. Our solution is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource tradeoffs of different possible gathering approaches, extracts information from both unstructured and structured documents. and uses the extracted information to refine its search and processing activities.