Distributed Intelligent Agents

Distributed Intelligent Agents Katia Sycara Keith Decker Anandeep Pannu Mike Williamson Da jun Zeng The Rob otics Institute Carnegie Mellon University Pittsburgh, PA 15213, U.S.A. Voice: 412 268-8825 Fax: 412 268-5569 URL: http://www.cs.cmu.edu/ softagents/ Abstract We are investigating techniques for developing distributed and adaptive collections of agents that co ordinate to retrieve, lter and fuse information relevant to the user, task and situation, as well as anticipate a user's information needs. In our system of agents, information gathering is seamlessly integrated with decision supp ort. The task for which particular information is requested of the agents do es not remain in the user's head but it is explicitly represented and supp orted through agent collab oration. In this pap er we present the distributed system architecture, agent collab oration interactions, and a reusable set of software comp onents for constructing agents. We call this reusable multi-agent computational infrastructure RETSINA Reusable Task Structure-based Intelligent Network Agents. It has three typ es of agents. Interface agents interact with the user receiving user sp eci cations and delivering results. They acquire, mo del, and utilize user preferences to guide system co ordination in supp ort of the user's tasks. Task agents help users p erform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. Wehave implemented this system framework and are developing collab orating agents in diverse complex real world tasks, such as organizational decision making the PLEIADES system, and nancial p ortfolio management the WARREN system. 1 Intro duction E ective use of the Internet byhumans or decision supp ort machine systems has b een hamp ered by some dominantcharacteristics of the Infosphere. First, information available from the net is unorganized, multi-mo dal, and distributed on server sites all over the world. Second, the numb er and variety of data sources and services is dramatically increasing every day.Fur- thermore, the availability,typ e and reliability of information services are constantly changing. Third, information is ambiguous and p ossibly erro- neous due to the dynamic nature of the information sources and p otential information up dating and maintenance problems. Therefore, information is b ecoming increasingly dicult for a p erson or machine system to collect, l- ter, evaluate, and use in problem solving. As a result, the problem of lo cating information sources, accessing, ltering, and integrating information in sup- p ort of decision making, as well as co ordinating information retrieval and problem solving e orts of information sources and decision-making systems has b ecome a very critical task. The notion of Intelligent Software Agents e.g., [1, 19 , 20 , 25 , 13 , 22] has b een prop osed to address this challenge. Although a precise de nition of an intelligent agent is still forthcoming, the currentworking notion is that Intelligent Software Agents are programs that act on b ehalf of their human users in order to p erform lab orious information gathering tasks, such as lo- cating and accessing information from various on-line information sources, resolving inconsistencies in the retrieved information, ltering away irrele- vantorunwanted information, integrating information from heterogeneous information sources and adapting over time to their human users' information needs and the shap e of the Infosphere. Most current agent-oriented approaches have fo cussed on what we call interface agents |a single agent with simple knowledge and problem solving capabilities whose main task is information ltering to alleviate the user's cognitiveoverload e.g., [15, 16 ]. Another typ e of agent is the Softbot [6], a single agent with general knowledge that p erforms a wide range of user-delegated information- nding tasks. 2 We b elieve that such centralized approaches have several limitations. A single general agentwould need an enormous amount of knowledge to b e able to deal e ectively with user information requests that cover a variety of tasks. In addition, a centralized system constitutes a pro cessing b ottleneck and a \single p oint of failure". Finally, b ecause of the complexity of the information nding and ltering task, and the large amount of information, the required pro cessing would overwhelm a single agent. Another prop osed solution is to address the problem by using multi-agent systems to access, lter, evaluate, and integrate this information [23 , 17 ]. Suchmulti-agent systems can compartmentalize sp ecialized task knowledge, organize themselves to avoid pro cessing b ottlenecks, and can b e built ex- pressly to deal with dynamic changes in the agent and information-source landscap e. In addition, multiple intelligent co ordinating agents are ideally suited to the predominantcharacteristics of the Infosphere, such as the het- erogeneity of the information sources, the diversity of information gathering and problem solving tasks that the gathered information supp orts, and the presence of multiple users with related information needs. We therefore b e- lieve that a distributed approach is sup erior, and p ossibly the only one that would work for information gathering and coherent information fusion. The context of multi-agent systems widens the notion of intelligent agent in at least two general ways. First, an agent's \user" that imparts goals to it and delegates tasks might b e not only a human but also another agent. Second, an agentmust have b een designed with explicit mechanisms for com- municating and interacting with other agents. Our notion is that suchmulti agent systems may comprise interface agents tied closely to an individual human's goals, task agents involved in the pro cesses asso ciated with arbi- trary problem-solving tasks, and information agents that are closely tied to a source or sources of data. An information agent is di erent from an interface agent in that an information agent is tied more closely to the data that it is providing, while an interface agent closely interacts with the user. Typ- ically, a single information agent will serve the information needs of many other agents humans or intelligent software agents. An information agent is also quite di erent from a typical World Wide Web WWW service that provides data to multiple users. Besides the obvious interface di erences, an information agent can reason ab out the way it will handle external requests and the order in which it will carry them out WWW services are typically blindly concurrent. Moreover, information agents not only p erform infor- 3 mation gathering in resp onse to queries but also can carry out long-term interactions that involve monitoring the Infosphere for particular conditions, as well as information up dating. In this pap er, we rep ort on our work on developing distributed collections of intelligent software agents that co op erate asynchronously to p erform goal-directed information retrieval and information integration in supp ort of p erforming a variety of decision making tasks [23 , 2]. Wehave b een developing RETSINA, an op en so ciety of reusable agents that self organize and co op erate in resp onse to task requirements. In particular, we will fo cus on three crucial characteristics of the overall framework that di erentiate our work from others: ours is a multi-agent system where the agents op erate asynchronously and collab orate with each other and their users, the agents actively seek out information, the information gathering is seamlessly integrated with problem solving and decision supp ort We will present the overall architectural framework, our agent design commitments, and agent architecture to enable the ab ovecharacteristics. We will draw examples from our work on Intelligent Agents in the domains of organizational decision making the PLEIADES system, and nancial p ortfolio management the WARREN system. The rest of the pap er is organized as follows. Section 2 brie y lists some agentcharacteristics we consider desirable. Section 3 motivates the distributed architecture for intelligent information retrieval and problem solving, and presents an overview of the system architecture, the di erenttyp es of agents in the prop osed multi agent organization, and agent co ordination mechanisms. Section 4 presents in detail the reusable agent architecture and discusses planning, control, and execution monitoring in agent op erations. Description and examples from the application of RETSINA to everyday organizational decision making and nancial p ortfolio management are given in Section 5. Section 6 presents concluding remarks. 4 2 Desirable Agent Characteristics Many di erent de nitions of intelligent agents have b een prop osed. In this section, we give a brief list of what we see as essential characteristics of intelligent agents. taskable. By "taskable" we mean agents that can take direction from humans or other agents. network-centric:by this we mean that agents should b e distributed and self organizing. When situations warrant it, agent mobilitymay also b e desirable. semi-autonomous rather than under direct human control all the time. For example, in an information gathering task, b ecause of the large amount of p otential requests for information, humans would b e swamp ed, if they had to initiate every single information request. The amountof agent autonomy should b e user controllable. persistent, i.e. capable of long p erio ds of unattended op eration. trustworthy: An agent should serve users' needs in a reliable wayso that users will develop trust in its p erformance. anticipatory: An agent should anticipate user information needs through task, role and situational mo dels as well as learning to serve as an intelligent cache, acquiring and holding information likely to b e needed.

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