Intention Scheduling for BDI Agent Systems - Semantic Scholar

Intention Scheduling for BDI Agent Systems Zu-Nien Lin, Hsun-Jen Hsu, and Feng-Jian Wang Department of Computer Science and Information Engineering, National Chiao Tung University Room 510, EC Building, 1001 Ta-Hsueh Road, Hsinchu City, Taiwan, ROC Telephone: 886-3-571-2121 ext 54718 Fax: 886-3-5724176 Email: {tnlin,hsuhj,fjwang}@csie.nctu.edu.tw

Abstract BDI is one of the models in Agent Systems. Currently, most of BDI agents’ researches are focused on the ability for agents to dynamically select plans to achieve a goal. There are fewer discussions on Intention Scheduling, the order in which the selected plans are executed. Agents must adapt to dynamical and unpredictable changes. Without a proper scheduling scheme, an agent may repeat unnecessary works, waste valuable resource or even fail the users' expect altogether. In this paper, we present an effective intention-scheduling scheme in BDI reasoning process, to make BDI agents more efficient and suitable for execution. Keywords: BDI Agent, Intention Scheduling, Time Constraint, Plan, Efficiency.

1. Background The BDI (Belief-Desire-Intention) theory is based on reasoning theory proposed by philosopher Michael Bratman [1]. As Cohen and Levesque [2] said, an agent could have many desires which all may not come true. A typical BDI agent has a set of Plans [3][4]. So Intentions could also be treated as the plans an agent has chosen for eventual execution. Rao and Georgeff [5] provided some logics for the BDI architecture.

2. Related Work AgentSpeak(XL) [6] provides another kind of PRSlike BDI agents which deal with the problem of intention scheduling with TÆMS[7]and DTC[8]. (see [9] for an overview of that approach to multi-agent systems.) Padgham and Thangarajah have some great works including representation and reasoning for goals [10], detecting resource conflicts [11], and detecting similar goals [12], interference between goals [13].

They created "Goal-Plan-Tree" structure to detect positive or negative interactions between goals.

3. Intention Scheduling Concept JAM[7] uses "Utility" to describe the importance of a goal and uses it as the only factor to select an intention. We define some terms for out intention scheduling. I-utility presents the importance of the goal for user. I-applicability indicates the extents the plan can fulfill the goal. An agent designer gives modifier Estimated Running Time, ERT, for describing how many logical time units a plan may need. User can also specify a Deadline Utility Function, DUF, formulating how the priority of the intention will be affected when its deadline is approaching. Degree of Completeness, DoC, is considered when there are negative interactions between intentions. In order to avoid the starvation problem, FairFacter should be considered but might only be considered for the intention which has no interaction with others.

4. Evaluation and Simulation Result Because the efficiency of scheduling heavily depends on the tasks the agent will receive, to show the effectiveness, we use a random task generator to issue goals and plans for the agents. Our simulations use different scheduling schemes: Normal: the utility of the top-level goals only Time-limited: the utility of the top-level goals and the deadline Interaction: the utility of the top-level goals and the interactions between the goals Interaction + Time-limited: consider the utility of the top-level goals, the deadline and the interactions between the goals. After the execution, the agent will report three values, which are:

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SU: the sum of utility of the goals that succeed / total utility. FU: the sum of utility of the goals that fail / total utility. TU: the sum of utility of the goals that are timeout / the sum of utility of the goals with deadline. Figure 1 shows the result of all four kinds of schemes when the Interaction Density varies form 0.1 to 0.5 and the Deadline Density is set to 0.3. The SU of Interaction+ Time-limited scheme remains the highest among the four.

[4] Marcus J. Huber. “JAM: A BDI-theoretic Mobile Agent Architecture.” In Proceedings of the third annual conference on Autonomous Agents, Seattle, Washington, United States, 1999. ACM Press. ISBN 1-58113-066-x, pp. 236-243. [5] Anand S. Rao and Michael P. Georgeff. “Modeling Rational Agents within a BDI Architecture.” Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning (KR'91), San Mateo, CA, USA, 1991. Morgan Kaufmann. ISBN 1-55860165-1, pp. 473-484. [6] Rafael H. Bordini, Ana L.C. Bazzan, Rafael de O. Jannone, Daniel M. Basso, Rosa M. Vicari.” AgentSpeak(XL): Efficient Intention Selection in BDI Agents via Decision-Theoretic Task Scheduling.” (AAMAS’02), Bologna, Italy, pp.1294-1302. [7] K. S. Decker and V. R. Lesser. “Quantitative modeling of complex environments.” International Journal of Intelligent Systems in Accounting, Finance and Management, 2(4), 1993, pp. 215–234.

Figure 1 SU of all schemes while Interaction Density changes

5. Conclusion & Future Work We aimed at developing an effective intentionscheduling scheme, discussing the various factors affecting the intention scheduling, and proposing an Intention Tree model to describe the structure and behavior of the agent. There are several problems not thoroughly discussed in this paper. In the future, we plan to delve into these problems and integrate them to produce a more efficient BDI agent system.

6. References [1] Paolo Busetta and Kotagiri Ramamohanarao, “An Architecture for mobile BDI Agents.” Proceedings of the 1998 ACM symposium on Applied Computing, 1998, pp. 445-452. [2] Philip R. Cohen and Hector J. Levesque. “Intention is Choice with Commitment.” Artificial Intelligence, March 1990, pp. 213-261. [3] Michael P. Georgeff and Amy L. Lansky. “Reactive Reasoning and Planning.” In Proceedings of the Sixth National Conference of the American Association for Arti_cial Intelligence (AAAI'87), volume 2, Seattle, WA, Morgan Kaufmann, July 1987, pp. 677-682

[8] T. Wagner, A. Garvey, and V. Lesser. “Criteria-directed heuristic task scheduling.” International Journal of Approximate Processing, Special Issue on Scheduling,19(1– 2), 1998, pp. 91–118. [9] V. R. Lesser. “Reflections on the nature of multi-agent coordination and its implications for agent architecture.” Autonomous Agents and Multi-Agent Systems, 1(1), 1998, pp. 89–111. [10] John Thangarajah, Lin Padgham and James Harland. “Representation and Reasoning for Goals in BDI Agents.” In Twenty-Fifth Australasian Computer Science Conference (ASCS2002), Melbourne, Australia. [11] John Thangarajah, Michael Winikoff , Lin Padgham and Klaus Fischer. “Avoiding Resource Conflicts in Intelligent Agents.” Proceedings of the 15th European Conference on Artificial Intelligence (ECAI 2002, Lyon, France), pp. 18-22. [12] John Thangarajah, Michael Winikoff and Lin Padgham. “Detecting & Exploiting Positive Goal Interaction in Intelligent Agents.” In Proceedings of the 2nd international joint conference on Autonomous agents and multi-agent systems (AAMAS ’03), Melbourne, Australia, pp. 401-408 [13] John Thangarajah, Lin Padhgam, Michael Winikoff. “Detecting and Avoiding Interference Between Goals in Intelligent Agents.” In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI 2003): Acapulco, Maxcio, pp.721-726.

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