and corresponding mutual exclusion rules  subsequent versions handle it directly  Koehler We used the AIPS version of IPP as well as the later version SGP
Sensory Graph Plan   Weld  Anderson   
Smith also extends  Graphplan to a richer domain description language  primarily focusing on uncertainty and sensing  As with IPP some of this transformation is performed using expansion techniques to remove quantification SGP also directly supports negated preconditions and conditional effects SGP tends to be slower  it is implemented in Common Lisp instead of C  than some of the other Graphplan based planners  We used SGP version b extends the Graphplan algorithm in part by STAN
STate ANalysis   Fox    Long adding a preprocessor  called TIM  to infer type information about the problem and domain This information is then used within the planning algorithm to reduce the size of the search space that the Graphplan algorithm would search STAN also incorporated optimized data structures  bit vectors of the planning graph  that help avoid many of the redundant calcu lations performed by Graphplan Additionally STAN maintains a wave front during graph construction to track remaining goals and so limit graph construction Subsequent versions incorporated further analyses eg symmetry exploitation and an additional simpler plan nng engine  Four versions of STAN were tested  the AIPS   competition version  version s and a development snapshot of version version black box K autz Selman converts planning problems into Boolean satisfiability problems  which are then solved using a variety of different techniques  The user indicates which techniques should be tried in what order  In constructing the satisfiability problem blackbox uses the planning graph constructed as in Graphplan  For blackbox  we used and version version b HSP
Heuristic Search Planner   Bonet    Geffner is based on heuristic search   The planner uses a variation of hill climbing with random restarts to solve planning problems The heuristic is based on using the Graphplan algorithm to solve a relaxed form of the planning problem  In this study  we used version which is an algorithmic refinement of the version entered into the AIPS competition and version 5 The Prodigy Research Group Prodigy combines state space planning with back ward chaining from the goal state A plan under construction consists of a head plan of totally ordered actions starting from the initial state and a tail plan of partially ordered actions related to the goal state Although not officially entered into the competition in formal results presented at the AIPS competition suggested that Prodigy performed well in comparison to the entrants  We used Prodigy version Barrett Golden Penberthy UCPOP
Weld is a Partial Order Causal Link planner The decision to include UCPOP was based on several factors First it does not expand quantifiers and negated preconditions  for some domains  the expansion from grounding operators can be so great as to make the problem insolvable  Second  UCPOP
is based on a significantly different algorithm in which interest has recently resurfaced  We used UCPOP version 5. We thank Eugene Fink for code that translates PDDL to Prodigy.