Facilities and Equipment

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. 
In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements.

ASAP, is an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings--planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.

Research into techniques that reformulate problems to make general solvers more efficiently derive solutions has attracted much attention, in particular when the reformulation process is to some degree solver and domain independent. There are major challenges to overcome when applying such techniques to automated planning, however: reformulation methods such as adding macro-operators (macros, for short) can be detrimental because they tend to increase branching factors during solution search, while other methods such as learning entanglements can limit a planner's space of potentially solvable problems (its coverage) through over-pruning. These techniques may therefore work well with some domain-problem-planner combinations, but work poorly with others.

MUM is an outstanding technique for synthesising macros from training examples in order to improve the speed and coverage of domain independent automated planning engines. MUM embodies domain – independent constraints for selecting macro candidates, for generating macros, and for limiting the size of the grounding set of learned macros, therefore maximising the utility of used macros. Our empirical results with IPC benchmark domains and a range of state of the art planners demonstrate the advance that MUM makes to the increased coverage and efficiency of the planners. Comparisons with a previous leading macro learning mechanism further demonstrate MUM's capability.

(in collaboration with Federico Cerutti, University of Aberdeen)

SemOpt is a novel SAT-based approach for preferred extension enumeration in abstract argumentation. It is based on a depth-first search in the space of complete extensions to identify those that are maximal, namely the preferred extensions. Each step of the search process requires the solution of a SAT problem through invocation of a SAT solver. More precisely, the algorithm is based on the idea of encoding the constraints corresponding to complete labellings of an AF as a SAT problem and then iteratively producing and solving modified versions of the initial SAT problem according to the needs of the search process.