In this paper an object-centric perspective on planning domain definition is presented along with an overview of GIPO (graphical interface for planning with objects), a supporting tools environment. It is argued that the object-centric view assists the domain developer in conceptualizing the domain's structure, and we show how GIPO enables the developer to capture that conceptualization at an appropriate and matching conceptual level. GIPO is an experimental environment which provides a platform for exploring and demonstrating the range and scope of tools required to support the knowledge engineering aspects of creating and validating planning systems, both for classical pre-condition planning and hierarchical planning. GIPO embodies the objectcentric view, leading to a range of benefits typically associated with object-oriented methods in other fields of software engineering such as highly visual development methods, code reuse and efficient, reliable development.

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, for instance, in the form of macro-operators or entanglements.In this paper we propose ASAP, 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.

In recent years the field of automated planning has significantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of configuring and exploiting a portfolio of planners to perform better than any individual planner: some recent planning systems based on this idea achieved significantly good results in experimental analysis and International Planning Competitions. Such results let us suppose that future challenges of the Automated Planning community will converge on designing different approaches for combining existing planning algorithms. This paper reviews existing techniques and provides an exhaustive guide to portfolio-based planning. In addition, the paper outlines open issues of existing approaches and highlights possible future evolution of these techniques.

This paper introduces a novel path planning technique called MCRT which is aimed at non-deterministic, partially known, real-time domains populated with dynamically moving obstacles, such as might be found in a real-time strategy (RTS) game. The technique combines an efficient form of Monte-Carlo tree search with the randomized exploration capabilities of rapidly exploring random tree (RRT) planning. The main innovation of MCRT is in incrementally building an RRT structure with a collision-sensitive reward function, and then re-using it to efficiently solve multiple, sequential goals. We have implemented the technique in MCRT-planner, a program which solves non-deterministic path planning problems in imperfect information RTS games, and evaluated it in comparison to four other state of the art techniques. Planners embedding each technique were applied to a typical RTS game and evaluated using the game score and the planning cost. The empirical evidence demonstrates the success of MCRT-planner.

Our research concerns formal, expressive, objectcentred languages and tools for use in engineering domains for planning applications. In this paper we extend our recent work on an object-centred language for encoding precondition planning domains to a language called OCL h , designed for HTN planning. Domain encodings for HTN planners are particularly troublesome, because they tend to be used in knowledged-based applications requiring a great deal of 'domain engineering', and the abstract operators central to an HTN model do not share the fairly clear declarative semantics of concrete pre-and post condition operators. Central to our approach is the development, in parallel, of the abstract operator set and the hierarchical state specification of the objects that the operators manipulate. In this paper we define and illustrate a transparency property, together with a transparency checking tool, which helps the developer to encode a clear planning model in OCL h . Our encoding of the Translog domain is used as an extended example to illustrate the approach. sorts¨physical obj© v ehicle© package© crane© plane ramp sorts¨vehicle© l and carrier© airplane© train sorts¨land carrier© m ail© flatbed© regular© tanker