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By Rachid Alami, Silvia Silva da Costa Bothelho (auth.), Michael Beetz, Joachim Hertzberg, Malik Ghallab, Martha E. Pollack (eds.)

In fresh years, self sustaining robots, together with Xavier, Martha [1], Rhino [2,3], Minerva,and distant Agent, have proven extraordinary functionality in long term demonstrations. In NASA’s Deep area application, for instance, an - tonomous spacecraft controller, referred to as the distant Agent [5], has autonomously played a scienti?c test in area. At Carnegie Mellon collage, Xavier [6], one other self sustaining cellular robotic, navigated via an o?ce - vironment for greater than a yr, permitting humans to factor navigation instructions and video display their execution through the web. In 1998, Minerva [7] acted for thirteen days as a museum tourguide within the Smithsonian Museum, and led a number of thousand humans via an exhibition. those self sufficient robots have in universal that they depend on plan-based c- trol to be able to in achieving higher problem-solving competence. within the plan-based procedure, robots generate keep watch over activities via holding and executing a plan that's e?ective and has a excessive anticipated software with admire to the robots’ c- lease targets and ideology. Plans are robotic keep watch over courses robotic cannot merely execute but additionally cause approximately and control [4]. therefore, a plan-based c- troller is ready to deal with and adapt the robot’s meant plan of action — the plan — whereas executing it and will thereby greater in attaining complicated and altering tasks.

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Extra info for Advances in Plan-Based Control of Robotic Agents: International Seminar Dagstuhl Castle, Germany, October 21–26, 2001 Revised Papers

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Singh. Between MDPs and Semi-MDPs: Learning, planning, and representing knowledge at multiple temporal scales. Journal of AI Research, 1998. SV96. Peter Stone and Manuela Veloso. Collaborative and adversarial learning: A case study in robotic soccer. In Sandip Sen, editor, Working Notes for the AAAI Symposium on Adaptation, Co-evolution and Learning in Multiagent Systems, pages 88–92, Stanford University, CA, March 1996. SV99a. P. Stone and M. Veloso. Task decomposition and dynamic role assignment for real-time strategic teamwork.

At execution time we only use situated and reactive plan execution. Consider a particular game situation within a particular play, which is admittedly well beyond the competence of current autonomous robotic soccer teams. 32 Michael Beetz and Andreas Hofhauser In the final phase of a play a robot is supposed to dribble the ball towards the goal and shoot a goal. Programming a control routine that specifies how to dribble towards the goal, when and how to shoot is very difficult and tedious and it is unlikely that such a program will achieve very good performance.

Programming a control routine that specifies how to dribble towards the goal, when and how to shoot is very difficult and tedious and it is unlikely that such a program will achieve very good performance. In particular, the decision criteria might also depend on the goal keeper strategy of the opponent goal keeper. The acquisition of such a routine is obviously a learning task. Having learned this task the performance might still be not sufficient and therefore we modify the whole play in order to use a teammate as a supporting player.

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