Human-Aware Plan Recognition

Plan recognition aims to recognize target plans given observed actions with history plan libraries or domain models in hand. Despite of the success of previous plan recognition approaches, they all neglect the impact of human preferences on plans. For example, a kid in a shopping mall might prefer to "executing" a plan of playing in water park, while an adult might prefer to "executing" a plan of having a cup of coffee. It could be helpful for improving the plan recognition accuracy to consider human preferences on plans. We assume there are historical rating scores on a subset of plans given by humans, and action sequences observed on humans. We estimate unknown rating scores based on rating scores in hand using an off-the-shelf collaborative filtering approach. We then discover plans to best explain the estimated rating scores and observed actions using a skip-gram based approach. In the experiment, we evaluate our approach in three planning domains to demonstrate its effectiveness.

[1]  Ya'akov Gal,et al.  Plan Recognition in Virtual Laboratories , 2011, IJCAI.

[2]  Qiang Yang,et al.  Learning action models from plan examples using weighted MAX-SAT , 2007, Artif. Intell..

[3]  Subbarao Kambhampati,et al.  Action-Model Based Multi-agent Plan Recognition , 2012, NIPS.

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

[5]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[6]  Subbarao Kambhampati,et al.  AI-MIX: Using Automated Planning to Steer Human Workers Towards Better Crowdsourced Plans , 2014, HCOMP.

[7]  Fahiem Bacchus,et al.  Exploiting the Power of mip Solvers in maxsat , 2013, SAT.

[8]  Froduald Kabanza,et al.  Controlling the Hypothesis Space in Probabilistic Plan Recognition , 2013, IJCAI.

[9]  Lei Li,et al.  Multi-Agent Plan Recognition with Partial Team Traces and Plan Libraries , 2011, IJCAI.

[10]  Robert P. Goldman,et al.  A probabilistic plan recognition algorithm based on plan tree grammars , 2009, Artif. Intell..

[11]  Philip R. Cohen,et al.  Sketch-Thru-Plan , 2015, Commun. ACM.

[12]  Subbarao Kambhampati,et al.  Discovering Underlying Plans Based on Distributed Representations of Actions , 2016, AAMAS.

[13]  Henry A. Kautz,et al.  Real-time crowd labeling for deployable activity recognition , 2013, CSCW.

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

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

[16]  Qiang Yang,et al.  Learning complex action models with quantifiers and logical implications , 2010, Artif. Intell..

[17]  Jun Zhang,et al.  Simlarity Search for Web Services , 2004, VLDB.

[18]  Hung Hai Bui,et al.  A General Model for Online Probabilistic Plan Recognition , 2003, IJCAI.

[19]  James F. Allen,et al.  TRAINS-95: Towards a Mixed-Initiative Planning Assistant , 1996, AIPS.