Dynamic Polymorphic Agents Scheduling and Execution Using Artificial Immune Systems
When a set of heterogeneous agents is considered to solve different kinds of problems, it is very challenging to specify the necessary number of elements, which functionally of each one will be used and the schedule of these actions in order to solve these problems. To deal with scenarios like this, the present article suggests an innovation at the Intelligent Agent Theory, a new concept called Dynamic Polymorphic Agent (DPA). This approach implies on the dynamic generation of one agent, built from the cooperation of existing agents and specific to fulfill the demanding task. To create this new entity, a monitor identifies and reads information regarding the functionalities of available agents present in the scene and, when a new problem is presented, it generates a task list to solve it. This list and the agents whose functionalities are necessary to solve the problem generate the new polymorphic agent. To fulfill this approach, two major paradigms are used: Aspect-Oriented Program (AOP) and Artificial Immune System (AIS).
KeywordsPolymorphic Agent IA Planning Artificial Immune Systems (AIS) multi-agent systems Aspect-Oriented Program (AOP)
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- 1.Tabuada, P., Pappas, G.J., Lima, P.: Motion Feasibility of Multi-Agent Formations. IEEE Transactions on Robotics 21(3) (2005)Google Scholar
- 2.Bagnall, A.J., Smith, G.D.: A multiagent Model of the UK Market in Electricity Generation. IEEE Transactions on Evolutionary Computation 9(5) (2005)Google Scholar
- 5.Russel, K., Norvig, P.: Artificial Intelligence, A Modern Aproach. In: Planning, ch. 11, pp. 337–366. Prentice Hall, Englewood CliffsGoogle Scholar
- 6.Hoffmann, J.: FF: The Fast-Forward Planning System. AI Magazine 22(3), 57–62 (2001)Google Scholar
- 7.Do, M.B., Khambhampati, S.: Solving planning-graph by compiling it into csp. In: Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling (2000)Google Scholar
- 8.Gerevini, A., Serina, I.: LPG: a Planner based on Local Search for Planning Graphs with Action Costs. In: Proceedings of the 6th International Conference on Artificial Intelligence Planning Systems, AIPS 2002, pp. 13–22 (2002)Google Scholar
- 12.Friedman, M., Kandel, A.: Introduction to pattern recognition: statistical, structural, neural and fuzzy logic approaches. World Scientific Publishing, London (2000)Google Scholar
- 13.Honorio, L.M., Dias, W., Freire, M., Souza, L.E.: Virtual Manufacturing System, Program and Video Tutorials (in Portuguese), www.virtualmanufacturing.unifei.edu.br (Project CNPq/CT-Info 400842/2003-3) (online since 2006)