Adaptive Applications of Intelligent Agents(6)

The paper presents two applications of intelligent agents to support the concept of adaptation defined in previous work. The concept of adaptation differs from the concept of intelligence and they do not necessarily associated with each other. Agent adapta

The agent in both cases is a program makes use of a set of heuristics to control one or more learning systems. A data structure is associated with each agent to store intermediate information that may help during the adaptation process. The agent utilizes also a set of parameters, cost functions, and other criteria to perform the adaptive process. The first agent, a travel agent, adapts existing knowledge-base describing preferred flights to fit new and unknown situations. The agent repeatedly changes the cost functions to simulate different possibilities of the given situation. For each situation, the agent runs the AQDT-2 program and stores information about the results. The agent uses different criteria to evaluate these results and to determine the best solution. The output of such methodology can be mapped to different external actions (e.g., reserve a ticket on flight number 534 on TWA, print a hotel accommodation form for one night) and/or other internal actions (e.g., ask the hotel agency to reserve a one night for the current customer).

An identification agent adapts plans for recognizing visual object. It generates dynamic plans for utilizing a set of systems or classifiers for recognizing visual objects. The goal of the agent is to determine the best classifier for recognizing a given object without knowing any information about the given object. Once the recognition is made the agent transform these results into external action to either open a door or a safe. The agent uses also a set of cost functions and data structures to optimize the plans. Plans generated by the agent describe different phases of the process of recognizing objects. This methodology could be applied to many applications and the recognition results can be mapped into many different external actions.

The two agents demonstrate different aspects of the proposed framework. The adaptation process in both agents is mainly internal, however, its results can be used in many ways to reflect adaptive behavior (externally). Both agents follow the general architecture proposed by the framework for intelligent adaptive agents. The cause of adaptation in both agents is the evolution of new task. The goal of each agent is defined by the service it provides to the user. In the travel agent, the agent adapt knowledge-base, cost functions, parameters and other criteria. In the identification agent, adaptation occurs in the architecture of the agent. The structure of tools used by the agent to accomplish the given task is dynamic. A future work is to analyze the proposed framework and extended to cover different issues in multi-agent societies.

REFERENCES

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[2] Decker, K., Sycara, K., and Williamson, M., "Intelligent Adaptive Information Agents", AAAI Technical Report No. WS-96-04, Imam, I.F. (Edr.), pp. , AAAI-Press, 1996.

[3] Haigh, K., and Veloso, M., "Using Perception Information for Robot Planning and Execution", AAAI Technical Report No. WS-96-04, Imam, I.F. (Edr.), pp. , AAAI-Press, 1996.

[4] Haynes, T., and Sen, S., "Adaptation Using Cases in Cooperative Groups", AAAI Technical Report No. WS-96-04, Imam, I.F. (Edr.), pp. , AAAI-Press, 1996.

[5] Imam, I.F., and Gutta, S.V., “A Hybrid Learning Approach for Better Recognition of Visual Objects”, Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, OR, AAAI press, 1996.

[6] Laird, J., Pearson, D., and Huffman, S., "Knowledge-directed Adaptation in Multi-level Agents", AAAI Technical Report No. WS-96-04, Imam, I.F. (Edr.), pp. , AAAI-Press, 1996.

[7] Michalski, R.S., and Imam, I.F., “Learning Problem-Optimized Decision Trees from Decision Rules: The AQDT-2 System”, Lecture Notes in Artificial Intelligence, No. 869, Ras, Z.W., and Zemankova, M., (Eds.), pp. 416-426, Springer Verlag, 1994.

[8] Michie, D., Muggleton, S., Page, D. and Srinivasan, A., “International East-West Challenge”, Oxford University, UK, 1994.

[9] Minsky, M., “A Conversation with Marvin Minsky about Agents”, Communications of the ACM, Vol. 37, No. 7, pp. 23-29, July, 1994.

[10] Rus, D., Gray, R., and Kotz, D., "Autonomous and Adaptive Agents that Gather Information", AAAI Technical Report No. WS-96-04, Imam, I.F. (Edr.), pp. , AAAI-Press, 1996.

[11] Simmons, R., Goodwin, R., Haigh, K.Z., Koenig, S., and O'Sullivan, J., "A Modular Architecture for Office Delivery Robots", Proceedings of the First International Conference on Autonomous Agents, 1997. (To Appear) [12] Veloso, M., M., Carbonell, J., Perez, M.A. Borrajo, D., Fink, E., and Blythe, J., "Integrating planning and learning: The Prodigy architecture", in the Journal of Experimental and Theoretical Artificial Intelligence, pp. 81-120, Vol. 7, No. 1, January, 1995.

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