State Projection via AI Planning

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-asplanning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of finding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.

[1]  Honray Lin,et al.  The untapped promise of digital mind maps , 2012, CHI.

[2]  Shirin Sohrabi,et al.  Finding Diverse High-Quality Plans for Hypothesis Generation , 2016, ECAI.

[3]  José Reinaldo Silva,et al.  itSIMPLE 2.0: An Integrated Tool for Designing Planning Domains , 2007, ICAPS.

[4]  Andrew Coles,et al.  Temporal Planning with Preferences and Time-Dependent Continuous Costs , 2012, ICAPS.

[5]  RepairSheila A. McIlraithDepartment.  Towards a Theory of Diagnosis , Testing and , 1994 .

[6]  Marie-Odile Cordier,et al.  Event-Based Diagnosis for Evolutive Systems , 1994 .

[7]  Robert P. Goldman,et al.  A New Model of Plan Recognition , 1999, UAI.

[8]  Alexandra Coman,et al.  Generating Diverse Plans Using Quantitative and Qualitative Plan Distance Metrics , 2011, AAAI.

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

[10]  T. L. McCluskey,et al.  Planning domain definition using GIPO , 2007, The Knowledge Engineering Review.

[11]  Patrik Haslum,et al.  Alarm processing with model-based diagnosis of event discrete systems , 2011, AIIP '11.

[12]  Raymond J. Mooney,et al.  Plan, Activity, and Intent Recognition: Theory and Practice , 2014 .

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

[14]  Deepak S. Turaga,et al.  Planning-Based Reasoning for Automated Large-Scale Data Analysis , 2015, ICAPS.

[15]  Husain Aljazzar,et al.  K⁎: A heuristic search algorithm for finding the k shortest paths , 2011, Artif. Intell..

[16]  Craig A. Knoblock,et al.  PDDL-the planning domain definition language , 1998 .

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

[18]  Shirin Sohrabi,et al.  Plan Recognition as Planning Revisited , 2016, IJCAI.

[19]  Subbarao Kambhampati,et al.  Refining Incomplete Planning Domain Models Through Plan Traces , 2013, IJCAI.

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

[21]  Malte Helmert,et al.  Optimal Planning for Delete-Free Tasks with Incremental LM-Cut , 2012, ICAPS.

[22]  Subbarao Kambhampati,et al.  Generating diverse plans to handle unknown and partially known user preferences , 2012, Artif. Intell..

[23]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[24]  Alban Grastien.  Alarm Processing with Model-Based Diagnosis of Discrete Event Systems , 2011 .

[25]  Jorge A. Baier,et al.  Preferred Explanations: Theory and Generation via Planning , 2011, AAAI.

[26]  Shirin Sohrabi,et al.  Hypothesis Exploration for Malware Detection Using Planning , 2013, AAAI.