A Logic for Specifying Agent Actions and Observations with Probability

We propose a non-standard modal logic for specifying agent domains where the agent's actuators and sensors are noisy, causing uncertainty in action and perception. The logic is multi-modal, indexed with actions; the logic is also augmented with observation objects to facilitate knowledge engineers dealing with explicit observations in the environment, and it includes a notion of probability. A tableau method is provided for proving decidability of the proposed logic. It is our conjecture that the tableau rules are complete with respect to the semantics. The proof does not yet exist, however, we discuss the current approach of the proof and provide some examples to motivate our conjecture.

[1]  Craig Boutilier,et al.  Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations , 1996, AAAI/IAAI, Vol. 2.

[2]  Alex M. Andrew,et al.  Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems , 2002 .

[3]  Andreas Herzig,et al.  Formalizing Action and Change in Modal Logic I: the frame problem , 1999, J. Log. Comput..

[4]  Thomas Lukasiewicz,et al.  Reasoning about actions with sensing under qualitative and probabilistic uncertainty , 2004, TOCL.

[5]  David Poole,et al.  Decision Theory, the Situation Calculus and Conditional Plans , 1998, Electron. Trans. Artif. Intell..

[6]  Edward J. Sondik,et al.  The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..

[7]  Ronald Fagin,et al.  Reasoning about knowledge and probability , 1988, JACM.

[8]  Alexander Ferrein,et al.  A Logic for Reasoning about Actions and Explicit Observations , 2010, Australasian Conference on Artificial Intelligence.

[9]  Craig Boutilier,et al.  Decision-Theoretic Planning: Structural Assumptions and Computational Leverage , 1999, J. Artif. Intell. Res..

[10]  Gerhard Lakemeyer,et al.  Cognitive Robotics , 2008, Handbook of Knowledge Representation.

[11]  W. Lovejoy A survey of algorithmic methods for partially observed Markov decision processes , 1991 .

[12]  G. Lakemeyer,et al.  Logic for specifying partially observable stochastic domains , 2011 .

[13]  George E. Monahan,et al.  A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 2007 .

[14]  Saul Kripke,et al.  A completeness theorem in modal logic , 1959, Journal of Symbolic Logic.

[15]  Johan van Benthem,et al.  Dynamic Update with Probabilities , 2009, Stud Logica.

[16]  Eyal Amir,et al.  Probabilistic Modal Logic , 2007, AAAI.

[17]  Blai Bonet,et al.  Planning and Control in Artificial Intelligence: A Unifying Perspective , 2001, Applied Intelligence.

[18]  N. Malcolm On Knowledge and Belief , 1954 .

[19]  Joseph Y. Halpern Reasoning about uncertainty , 2003 .

[20]  Wiebe van der Hoek,et al.  First steps in modal logic , 1997 .

[21]  Craig Boutilier,et al.  Decision-Theoretic, High-Level Agent Programming in the Situation Calculus , 2000, AAAI/IAAI.

[22]  Max J. Cresswell,et al.  A New Introduction to Modal Logic , 1998 .

[23]  Gavin Rens A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains , 2010 .

[24]  A. Chagrov,et al.  Modal Logic (Oxford Logic Guides, vol. 35) , 1997 .

[25]  Frank Wolter,et al.  Handbook of Modal Logic, Volume 3 (Studies in Logic and Practical Reasoning) , 2006 .

[26]  Hector J. Levesque,et al.  Reasoning about Noisy Sensors and Effectors in the Situation Calculus , 1995, Artif. Intell..

[27]  Scott Sanner,et al.  Symbolic Dynamic Programming for First-order POMDPs , 2010, AAAI.

[28]  Gerhard Lakemeyer,et al.  ESP: A Logic of Only-Knowing, Noisy Sensing and Acting , 2007, AAAI.

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

[30]  Richard Spencer-Smith,et al.  Modal Logic , 2007 .

[31]  Michael Zakharyaschev,et al.  Modal Logic , 1997, Oxford logic guides.

[32]  Chenggang Wang,et al.  Planning with POMDPs using a compact, logic-based representation , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).