Computer Science ›› 2015, Vol. 42 ›› Issue (1): 220-226.doi: 10.11896/j.issn.1002-137X.2015.01.049

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Learning Action Models Described in Action Language B by Combining ILP and ASP

LIU Zhen and ZHANG Zhi-zheng   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Action model learning is beneficial to autonomous and automated systems.If an Agent can update its action model according to the changes occurred in the environment,it can be more adaptable to the world and operate more effectively.Simultaneously,action model learning can provide modeling dynamic domain with an initial rough model which is the foundation for further improvement and modification.We designed an algorithm used for learning action models described in language B by combining ILP and ASP.This algorithm can work on dynamic domains consisting of objects of different scale.In the experiments,we tested the learning algorithm through using classic planning cases and verified the soundness of the learning algorithm.

Key words: Action model learning,Action language B,Inductive logic programming,Answer set programming

[1] Certicky M.Action Learning with Reactive Answer Set Pro-gramming:Preliminary Report[C]∥The Eighth International Conference on Autonomic and Autonomous Systems(ICAS 2012).2012:107-111
[2] Estlin T,Gaines D,Chouinard C,et al.Increased Mars rover autonomy using AI planning,scheduling and execution[C]∥IEEE International Conference on Robotics and Automation,2007.IEEE,2007:4911-4918
[3] Yang Q,Wu K,Jiang Y.Learning action models from plan ex-amples using weighted MAX-SAT[J].Artificial Intelligence,2007,171(2):107-143
[4] McCarthy J.Elaboration tolerance.1997-09-09.http:/www-formal.stanford.edu/juc./elaboration.html
[5] Gelfond M,Kahl Y.Knowledge Representation,Reasoning,and the Design of Intelligent Agents.http://redwood.cs.ttu.edu/~mgelfond/FALL-2012/book.pdf
[6] IPC(2003).http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume20/long03a-html/ node37.html
[7] Balduccini M.Learning Action Descriptions with A-Prolog:Ac-tion Language C[C]∥AAAI Spring Symposium:Logical Formalizations of Commonsense Reasoning.2007:13-18
[8] Gebser M,Grote T,Kaminski R,et al.Reactive answer set programming[M].Logic Programming and Nonmonotonic Reasoning.Springer Berlin Heidelberg,2011:54-66
[9] Wang X.Learning by observation and practice:An incremental approach for planning operator acquisition[C]∥ICML.1995:549-557
[10] Sil A,Yates A.Extracting STRIPS Representations of Actions and Events[C]∥RANLP.2011:1-8
[11] Boose J H,Gaines B R.Knowledge acquisition for knowledge-based systems:Notes on the state-of-the-art[J].Machine Learning,1989,4(3/4):377-394
[12] Benson S.Learning action models for reactive autonomous Agents[D].Stanford university,1996
[13] 谢颖.归纳逻辑程序设计初探[D].北京:北京师范大学哲学系,2008
[14] Stuart R,Peter N.人工智能—一种现代方法(第2版)[M].姜哲,金栾江,等译.北京:人民邮电出版社,2010
[15] Lorenzo D.Learning non-monotonic causal theories from narratives of actions[C]∥NMR.2002:349-355
[16] Pasula H,Zettlemoyer L S,Kaelbling L P.Learning Probabilistic Relational Planning Rules[C]∥ICAPS.2004:73-82
[17] Yang Q,Wu K,Jiang Y.Learning Actions Models from Plan Examples with Incomplete Knowledge[C]∥ICAPS.2005:241-250

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