Comparing the effect of pruning on a best path and a na?ve-approach blackboard solver

详细信息    查看全文

• 作者：Jeremy Straub
• 关键词：Blackboard Architecture ; blackboard maintenance ; control of heterogeneous craft ; multi ; craft control ; multi ; tier control
• 刊名：International Journal of Automation and Computing
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：12
• 期：5
• 页码：503-510
• 全文大小：450 KB
• 参考文献：[1]B. Hayes-Roth. A blackboard architecture for control. Artificial Intelligence, vol. 26, no. 3, pp. 251-21, 1985.CrossRef
  [2]J. Straub. A data collection decision-making framework for a multi-tier collaboration of heterogeneous orbital, aerial, and ground craft. In Proceedings of SPIE 8742, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IV, SPIE, Bellingham, USA, 2013.
  [3]J. Straub, H. Reza. The use of the blackboard architecture for a decision making system for the control of craft with various actuator and movement capabilities. In Proceedings of the 11th International Conference on Information Technology: New Generations, IEEE, Las Vegas, USA, pp. 514-19, 2014.
  [4]J. Clarke, W. H. Chen. Trajectory generation for autonomous soaring UAS. International Journal of Automation and Computing, vol. 9, no. 3, pp. 248-56, 2012.CrossRef
  [5]C. J. Liu, W. H. Chen, J. Andrews. Experimental tests of autonomous ground vehicles with preview. International Journal of Automation and Computing, vol. 7, no. 3, pp. 342-48, 2010.CrossRef
  [6]T. K. Wang, Q. Dang, P. Y. Pan. Path planning approach in unknown environment. International Journal of Automation and Computing, vol. 7, no. 3, pp. 301-16, 2010.
  [7]B. K. Sahu, B. Subudhi. Adaptive tracking control of an autonomous underwater vehicle. International Journal of Automation and Computing, vol. 11, no. 3, pp. 299-07, 2014.CrossRef
  [8]U. K. Patel, P. Patel, H. Hexmoor, N. Carver. Improving behavior of computer game bots using fictitious play. International Journal of Automation and Computing, vol. 9, no. 2, pp. 122-34, 2012.CrossRef
  [9]L. D. Erman, F. Hayes-Roth, V. R. Lesser, D. R. Reddy. The hearsay-II speech-understanding system: Integrating knowledge to resolve uncertainty. ACM Computing Surveys, vol. 12, no. 2, pp. 213-53, 1980.CrossRef
  [10]D. A. Waterman. A Guide to Expert Systems, New York, NY, USA: Addison-Wesley, 1986.
  [11]B. Buchanan, D. Barstow, R. Bechtal, J. Bennett, W. Clancey, C. Kulikowski, T. Mitchell, D. Waterman. Constructing an expert system. Building Expert Systems, F. Hayes-Roth, D. Waterman, D. Lenat, Eds., New York, USA: Addison-Wesley, vol. 50, pp. 127-67, 1983.
  [12]H. Velthuijsen, B. Lippolt, J. Vonk. A parallel blackboard architecture. In Proceedings of the 3rd International Expert Systems Conference, Medford, USA, pp. 487-93, 1987.
  [13]K. S. Ettabaa, I. R. Farah, B. Solaiman, M. B. Ahmed. Distributed blackboard architecture for multi-spectral image interpretation based on multi-agent system. In Proceedings of Information and Communication Technologies, IEEE, Damascus, Syria, pp. 3070-075, 2006.
  [14]J. Palma, R. Marin, M. Balsa, S. Barro, P. Félix. A control model for distributed blackboard architecture based on task structures. In Proceedings of the International Symposium on Engineering of Intelligent Systems, pp. 476-83, 2001.
  [15]D. L. Larner. A distributed, operating system based, blackboard architecture for real-time control. In Proceedings of the 3rd International Conference on Industrial and Engineering Applications of Artificial Intelligence and expert Systems, ACM, New York, USA, vol. 1, pp. 99-08, 1990.
  [16]J. P. Rosenking, S. P. Roth. REACT: Cooperating expert systems via a blackboard architecture. In Proceedings of SPIE 0937, Applications of Artificial Intelligence VI, SPIE, Bellingham, USA, pp. 143-50, 1988.
  [17]J. Dong, S. Chen, J. J. Jeng. Event-based blackboard architecture for multi-agent systems. In Proceedings of IEEE International Conference on Information Technology: Coding and Computing, IEEE, Las Vegas, USA, vol. 2, pp. 379-84, 2005.
  [18]D. D. Corkill, K. Q. Gallagher, K. E. Murray. GBB: A generic blackboard development system. In Proceedings of the National Conference on Artificial Intelligence, Palo Alto, USA, pp. 1008-014, 1986.
  [19]S. D. Tynor, S. P. Roth, J. F. Gilmore. Implementation of a generic blackboard architecture. In Proceedings of SPIE 0786, Applications of Artificial Intelligence V, SPIE, Bellingham, USA, pp. 116-24, 1987.
  [20]I. D. Alexander-Craig. A New Interpretation of the Blackboard Architecture, Technical Report, University of Warwick, [Online], Available: http://?eprints.?dcs.?warwick.?ac.?uk/-368/-/?cs-rr-254.?pdf, 1993.
  [21]Y. C. Jiang, Z. Y. Xia, Y. P. Zhong, S. Y. Zhang. An adaptive adjusting mechanism for agent distributed blackboard architecture. Microprocessors and Microsystems, vol. 29, no. 1, pp. 9-0, 2005.CrossRef
  [22]H. Laasri, B. Maitre, T. Mondot, F. Charpillet, J. P. Haton. ATOME: A Blackboard Architecture with Temporal and Hypothetical Reasoning, Research Report RR-0855, Inria, Le Chesnay Cedex, France, [Online], Available: http://?hal.?archivesouvertes--fr/docs/00/07/71/80/PDF/RR-0855. pdf, 1988.
  [23]J. Rice. Poligon: A System for Parallel Problem Solving, Technical Report 86-9, Knowledge Systems Laboratory, Stanford University, Stanford, USA, [Online], A
• 作者单位：Jeremy Straub (1)
  
  1. Department of Computer Science, University of North Dakota, Grand Forks, North Dakota, 58202, USA
  
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Computer Applications
  Computer-Aided Engineering and Design
  Chinese Library of Science
  
• 出版者：Institute of Automation, Chinese Academy of Sciences, co-published with Springer-Verlag GmbH
• ISSN：1751-8520

文摘

A na?ve solver is one approach that can be used to identify prospective solutions based on data on (or projected to be on) a Blackboard Architecture’s blackboard. The na?ve solver approach doesn’t implement heuristics or other techniques to determine what solution paths to attempt first. Instead, it runs the blackboard forward (simulating what would occur if data were gradually added to the blackboard at a faster-than-real time rate). The approach doesn’t guarantee that an optimal solution will be found and will need to be run repetitively to create multiple solutions for comparison. This paper assesses the effect of pre-pruning the blackboard’s facts and rules to remove those that are not relevant (e.g., facts that cannot be asserted, rules that cannot be triggered) or which produce irrelevant facts and pruning actions that produce irrelevant facts (and/or trigger other similarly useless actions). It describes the Blackboard implementation and its utility, explains the pruning process used and presents quantitative and qualitative assessment of the utility of pruning to a na?ve solver’s operations. This value is extrapolated to facilitate consideration of a more robust pruning process which also removes low-value facts, actions and rules in addition to those being removed due to their uselessness. Keywords Blackboard Architecture blackboard maintenance control of heterogeneous craft multi-craft control multi-tier control