Goal Recognition Design with Non-Observable Actions

Goal recognition design involves the offline analysis of goal recognition models by formulating measures that assess the ability to perform goal recognition within a model and finding efficient ways to compute and optimize them. In this work we relax the full observability assumption of earlier work by offering a new generalized model for goal recognition design with non-observable actions. A model with partial observability is relevant to goal recognition applications such as assisted cognition and security, which suffer from reduced observability due to sensor malfunction or lack of sufficient budget. In particular we define a worst case distinctiveness (wcd) measure that represents the maximal number of steps an agent can take in a system before the observed portion of his trajectory reveals his objective. We present a method for calculating wcd based on a novel compilation to classical planning and propose a method to improve the design using sensor placement. Our empirical evaluation shows that the proposed solutions effectively compute and improve wcd.

[1]  Karen L. Myers,et al.  Identifying Terrorist Activity with AI Plan Recognition Technology , 2005, AI Mag..

[2]  Hector Geffner,et al.  Probabilistic Plan Recognition Using Off-the-Shelf Classical Planners , 2010, AAAI.

[3]  Hector Geffner,et al.  Goal Recognition over POMDPs: Inferring the Intention of a POMDP Agent , 2011, IJCAI.

[4]  Oren Etzioni,et al.  A Sound and Fast Goal Recognizer , 1995, IJCAI.

[5]  Derek Long,et al.  Domain Independent Goal Recognition , 2010, STAIRS.

[6]  Derek Long,et al.  Accurately determining intermediate and terminal plan states using bayesian goal recognition , 2011 .

[7]  Henry A. Kautz,et al.  Generalized Plan Recognition , 1986, AAAI.

[8]  Blai Bonet,et al.  A Concise Introduction to Models and Methods for Automated Planning , 2013, A Concise Introduction to Models and Methods for Automated Planning.

[9]  Mark S. Boddy,et al.  Course of Action Generation for Cyber Security Using Classical Planning , 2005, ICAPS.

[10]  Erez Karpas,et al.  Goal Recognition Design , 2014, ICAPS.

[11]  Milind Tambe,et al.  Towards Detection of Suspicious Behavior from Multiple Observations , 2011, Plan, Activity, and Intent Recognition.

[12]  Erez Karpas,et al.  Goal Recognition Design for Non-Optimal Agents , 2015, AAAI.

[13]  R. Goldman,et al.  Partial Observability and Probabilistic Plan/Goal Recognition , 2005 .

[14]  Hila Zarosim,et al.  Fast and Complete Symbolic Plan Recognition: Allowing for Duration, Interleaved Execution, and Lossy Observations , 2005 .

[15]  Carmel Domshlak,et al.  Landmarks, Critical Paths and Abstractions: What's the Difference Anyway? , 2009, ICAPS.

[16]  Malte Helmert,et al.  The Fast Downward Planning System , 2006, J. Artif. Intell. Res..

[17]  Hector Geffner,et al.  Plan Recognition as Planning , 2009, IJCAI.

[18]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[19]  Henry Kautz,et al.  Foundations of Assisted Cognition Systems , 2003 .