Conditional Epistemic Planning

Recent work has shown that Dynamic Epistemic Logic (DEL) offers a solid foundation for automated planning under partial observability and non-determinism. Under such circumstances, a plan must branch if it is to guarantee achieving the goal under all contingencies (strong planning). Without branching, plans can offer only the possibility of achieving the goal (weak planning). We show how to formulate planning in uncertain domains using DEL and give a language of conditional plans. Translating this language to standard DEL gives verification of both strong and weak plans via model checking. In addition to plan verification, we provide a tableau-inspired algorithm for synthesising plans, and show this algorithm to be terminating, sound and complete.

[1]  S. Hart,et al.  Handbook of Game Theory with Economic Applications , 1992 .

[2]  T. Lima Optimal methods for reasoning about actions and plans in multi-agent systems , 2007 .

[3]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[4]  van der Hoek,et al.  Semantic Results for Ontic and Epistemic Change , 2008 .

[5]  Frank Wolter,et al.  Handbook of Modal Logic , 2007, Studies in logic and practical reasoning.

[6]  Alexandru Baltag,et al.  A qualitative theory of dynamic interactive belief revision , 2008 .

[7]  J. Benthem Games in dynamic epistemic logic , 2001 .

[8]  Lawrence S. Moss,et al.  Logics for Epistemic Programs , 2004, Synthese.

[9]  Thomas Bolander,et al.  Epistemic planning for single- and multi-agent systems , 2011, J. Appl. Non Class. Logics.

[10]  Jussi Rintanen,et al.  Complexity of Planning with Partial Observability , 2004, ICAPS.

[11]  Michael Wooldridge,et al.  Logic and the Foundations of Game and Decision Theory - LOFT , 2009 .

[12]  Jenny Donovan,et al.  “Vicious Circles” , 2014, Qualitative health research.

[13]  Lawrence S. Moss,et al.  The Logic of Public Announcements and Common Knowledge and Private Suspicions , 1998, TARK.

[14]  Paolo Traverso,et al.  Automated planning - theory and practice , 2004 .

[15]  D. Bryce 6th International Planning Competition: Uncertainty Part , 2008 .

[16]  Wiebe van der Hoek,et al.  Dynamic Epistemic Logic and Knowledge Puzzles , 2007, ICCS.

[17]  Farokh B. Bastani,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Simple and Fast Strong Cyclic Planning for Fully-Observable Nondeterministic Planning Problems �� , 2022 .

[18]  Malik Ghallab,et al.  Chapter 14 – Temporal Planning , 2004 .

[19]  D. Harrison,et al.  Vicious Circles , 1995 .

[20]  Jan van Eijck,et al.  DEMO — A Demo of Epistemic Modelling , 2007 .

[21]  Ian Horrocks,et al.  Computational modal logic , 2007, Handbook of Modal Logic.

[22]  J.F.A.K. van Benthem,et al.  Dynamics odds and ends , 1998 .

[23]  Tran Cao Son,et al.  An Action Language for Reasoning about Beliefs in Multi-Agent Domains , 2012 .

[24]  Andreas Witzel,et al.  DEL Planning and Some Tractable Cases , 2011, LORI.

[25]  Paolo Traverso,et al.  Automated Planning: Theory & Practice , 2004 .

[26]  Enrico Pontelli,et al.  Reasoning about the Beliefs of Agents in Multi-agent Domains in the Presence of State Constraints: The Action Language mAL , 2013, CLIMA.

[27]  Robert E. Tarjan,et al.  Three Partition Refinement Algorithms , 1987, SIAM J. Comput..

[28]  Guillaume Aucher,et al.  An Internal Version of Epistemic Logic , 2010, Stud Logica.

[29]  Jelle Gerbrandy,et al.  Dynamic epistemic logic , 1998 .