Statement of research interest

The main objective of my research activity is the development and the experimental analysis of efficient techniques of domain independent planning, which proposes to identify a partially ordered set of actions whose execution allows one or more agents to reach their goals starting from a specific initial situation. My research has been focused on generic algorithms that can be applied to a variety of situations in which the evolution of the external world makes it necessary to revise a plan which has been previously formulated or the generation of a new plan. In such a context I have developed the systems LPG, LPG-td and ADJ together with Prof. Alfonso Gerevini and other members of the AI group of the University of Brescia. In particular LPG is at present one of the best domain independent planners that exist in terms of execution times, quality of the plans produced and expressivity of manageable problems. LPG took part in the third International Planning Competition IPC3 obtaining the award “distinguished performance of the first order”. LPG-td has been awarded in the fourth International Planning Competition IPC4 in the Suboptimal Metric Temporal Track. LPG-td is an extension of the LPG planner (Gerevini, Saetti, & Serina 2003; 2004) that can handle most of the features of PDDL2.2, the standard planning language of the 4th International Planning Competition (IPC-4). Like the previous version of LPG, the new version is based on a stochastic local search in the space of particular “action graphs” derived from the planning problem specification. In LPG-td, this graph representation has been extended to deal with the new features of PDDL2.2, as well to improve the management of durative actions and of numerical expressions. Our approach integrates constraint-based temporal reasoning into a recent planning framework based on action graphs and local search. The new plan representation is called TDA-graphs. We have developed a polynomial method for temporal constraint reasoning during search, which exploits the structure of the temporal information in the TDA-graph, and some new local search techniques for temporal planning through TDA-graphs. All our techniques are implemented in the LPG-td planner. An analysis of the IPC-4 results show that our planner performs very well compared to other recent temporal planners supporting deterministic exogenous events and derived predicates, both in terms of CPU-time required to find a plan and quality of the best plans that can be generated by our system1 (Gerevini, Saetti, & Serina 2005).

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