Beating LM-Cut with hmax (Sometimes): Fork-Decoupled State Space Search

Factored planning decouples planning tasks into subsets (factors) of state variables. The traditional focus is on handling complex cross-factor interactions. Departing from this, we introduce a form of target-profile factoring, forcing the cross-factor interactions to take the form of a fork, with several leaf factors and one potentially very large root factor. We show that forward state space search gracefully extends to such structure, by augmenting regular search on the root factor with maintenance of the cheapest compliant paths within each leaf factor. We analyze how to guarantee optimality. Connecting to standard heuristics, the performance improvements relative to A* are substantial, and sometimes dramatic: In four IPC benchmark domains, fork-decoupled state space search outperforms standard state space search even when using hmax in the former vs. LM-cut in the latter.

[1]  Olivier Buffet,et al.  Factored Planning Using Decomposition Trees , 2007, IJCAI.

[2]  Carmel Domshlak,et al.  Structural Patterns Heuristics via Fork Decomposition , 2008, ICAPS.

[3]  Álvaro Torralba,et al.  Constrained Symbolic Search: On Mutexes, BDD Minimization and More , 2013, SOCS.

[4]  Enrico Macii,et al.  Algebric Decision Diagrams and Their Applications , 1997, ICCAD '93.

[5]  Ronen I. Brafman,et al.  From One to Many: Planning for Loosely Coupled Multi-Agent Systems , 2008, ICAPS.

[6]  Bernhard Nebel,et al.  COMPLEXITY RESULTS FOR SAS+ PLANNING , 1995, Comput. Intell..

[7]  Ronen I. Brafman,et al.  On the complexity of planning for agent teams and its implications for single agent planning , 2013, Artif. Intell..

[8]  Ronen I. Brafman,et al.  Strucutre and Complexitiy in Planning with Unary Operators , 2000, PuK.

[9]  Bernhard Nebel,et al.  The FF Planning System: Fast Plan Generation Through Heuristic Search , 2011, J. Artif. Intell. Res..

[10]  Christer Bäckström,et al.  Incremental planning , 1996 .

[11]  Vincent Vidal,et al.  A Lookahead Strategy for Heuristic Search Planning , 2004, ICAPS.

[12]  Carmel Domshlak,et al.  Enhanced Symmetry Breaking in Cost-Optimal Planning as Forward Search , 2012, ICAPS.

[13]  Blai Bonet,et al.  Planning as heuristic search , 2001, Artif. Intell..

[14]  Kevin Leyton-Brown,et al.  SATzilla: Portfolio-based Algorithm Selection for SAT , 2008, J. Artif. Intell. Res..

[15]  Malte Helmert,et al.  The Relative Pruning Power of Strong Stubborn Sets and Expansion Core , 2013, ICAPS.

[16]  Patrik Haslum,et al.  Cost-Optimal Factored Planning: Promises and Pitfalls , 2010, ICAPS.

[17]  Jonathan Schaeffer,et al.  Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators , 2005, J. Artif. Intell. Res..

[18]  Craig A. Knoblock.  Automatically Generating Abstractions for Planning , 1994, Artif. Intell..

[19]  Fernando Fernández,et al.  IBACOP and IBACOP2 Planner , 2014 .

[20]  Malte Helmert,et al.  Efficient Stubborn Sets: Generalized Algorithms and Selection Strategies , 2014, ICAPS.

[21]  Enrico Macii,et al.  Algebraic decision diagrams and their applications , 1993, Proceedings of 1993 International Conference on Computer Aided Design (ICCAD).

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

[23]  Ronen I. Brafman,et al.  Tunneling and Decomposition-Based State Reduction for Optimal Planning , 2012, ECAI.

[24]  Eyal Amir,et al.  Factored planning , 2003, IJCAI 2003.

[25]  Ronen I. Brafman,et al.  Factored Planning: How, When, and When Not , 2006, AAAI.

[26]  Jörg Hoffmann,et al.  Resource-Constrained Planning: A Monte Carlo Random Walk Approach , 2012, ICAPS.

[27]  Ronen I. Brafman,et al.  Distributed Heuristic Forward Search for Multi-agent Planning , 2014, J. Artif. Intell. Res..

[28]  Malte Helmert,et al.  About Partial Order Reduction in Planning and Computer Aided Verification , 2012, ICAPS.

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