Case-Based Plan Diversity

The concept of diversity was successfully introduced for recommender-systems. By displaying results that are not only similar to a target problem but also diverse among themselves, recommender systems have been shown to provide more effective guidance to the user. We believe that similar benefits can be obtained in case-based planning, provided that diversity-enhancement techniques can be adapted appropriately. Our claim is that diversity is truly useful when it refers not only to the initial and goal states of a plan, but also to the sequence of actions the plan consists of. To formalize this characteristic and support our claim, we define the metric of “plan diversity” and put it to test using plans for a real-time strategy game, a domain chosen for the simplicity and clarity of its tasks and the quantifiable results it generates.

[1]  David W. Aha,et al.  Case-Based Reasoning in Transfer Learning , 2009, ICCBR.

[2]  Santiago Ontañón,et al.  ON‐LINE CASE‐BASED PLANNING , 2010, Comput. Intell..

[3]  Kristian J. Hammond,et al.  Chapter 8 – Case-based Planning , 1989 .

[4]  Carlo Strapparava,et al.  Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings , 2008, AH.

[5]  Barry Smyth,et al.  Advances in Case-Based Reasoning , 1996, Lecture Notes in Computer Science.

[6]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[7]  Hector Muñoz-Avila,et al.  Case-Based Plan Adaptation: An Analysis and Review , 2008, IEEE Intelligent Systems.

[8]  Hector Muñoz-Avila,et al.  Case-based planning , 2005, The Knowledge Engineering Review.

[9]  Hector Muñoz-Avila,et al.  Adaptation versus Retrieval Trade-Off Revisited: An Analysis of Boundary Conditions , 2009, ICCBR.

[10]  David W. Aha,et al.  Using Guidelines to Constrain Interactive Case-Based HTN Planning , 1999, ICCBR.

[11]  Luca Spalazzi,et al.  A Survey on Case-Based Planning , 2004, Artificial Intelligence Review.

[12]  Derek G. Bridge,et al.  Ways of Computing Diverse Collaborative Recommendations , 2006, AH.

[13]  Karen L. Myers Metatheoretic Plan Summarization and Comparison , 2006, ICAPS.

[14]  Barry Smyth,et al.  On the Role of Diversity in Conversational Recommender Systems , 2003, ICCBR.

[15]  Luca Spalzzi,et al.  A Survey on Case-Based Planning , 2001 .

[16]  David McSherry,et al.  Completeness Criteria for Retrieval in Recommender Systems , 2006, ECCBR.

[17]  Subbarao Kambhampati,et al.  Domain Independent Approaches for Finding Diverse Plans , 2007, IJCAI.

[18]  Barry Smyth,et al.  Similarity vs. Diversity , 2001, ICCBR.

[19]  Robin D. Burke,et al.  Interactive Critiquing forCatalog Navigation in E-Commerce , 2002, Artificial Intelligence Review.

[20]  Ivan Serina,et al.  Plan Stability: Replanning versus Plan Repair , 2006, ICAPS.

[21]  Manuela M. Veloso,et al.  Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning , 1997, ICCBR.

[22]  David McSherry,et al.  Diversity-Conscious Retrieval , 2002, ECCBR.