Automated Modelling and Reformulation in Planning

Lead Research Organisation: King's College London
Department Name: Informatics


Although AI Planning and Constraint Programming share many techniques and approaches, an important difference lies in the approach to modelling. In CP and also in Operations Research, modellers spend considerable time and effort evaluating alternative models and selecting representations of a problem that will make it most amenable to solution by existing technology. In Planning, researchers typically spend little time considering alternative models and are content to work with the first model they construct, working instead on improving the planning technology to try to tackle the problem, whatever its form. The reason for the strategy of planning researchers is that the intention is to avoid the need for expert planning knowledge in order to exploit a planner. However, the price for this strategy is that there is very little accumulated research expertise in the problem of modelling and no systematic comparison of the performance of planners using alternative models of the same problem. Although avoiding the need for expert planning knowledge in order to use a planner is an important goal, there is clearly a lost opportunity to identify ways in which models might be structured to be most amenable to solution. We propose to combine these strategies by exploring the automatic reformulation of planning problems in order to better exploit the existing planning technology by restructuring models to expose the information that can make a planner make more intelligent choices.


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Related Projects

Project Reference Relationship Related To Start End Award Value
EP/G023360/1 01/12/2008 01/11/2011 £335,650
EP/G023360/2 Transfer EP/G023360/1 02/11/2011 01/12/2012 £102,118
Description Our group was the first to start extending forward search planners towards solution of problems with continuous time and numeric quantities. Over a number of years, and funded by a sequence of projects, this has led to a very capable planning framework, called POPF, which is still leading the field in temporal and metric planning in mixed discrete-continuous domains.
Exploitation Route The POPF planner is capable of solving planning problems in any domain expressible in the PDDL family of languages. Since it can solve temporal and numeric problems, and problems involving linear continuous change, POPF is versatile and powerful, and is completely domain-independent. It is also by now very robust, having been tested on thousands of problems from across a wide range of domains.
Sectors Aerospace, Defence and Marine,Agriculture, Food and Drink,Chemicals,Construction,Energy,Environment,Healthcare,Manufacturing, including Industrial Biotechology,Transport,Other

Description The project has advanced the state of the art in modelling and solving planning problems, including problems with significantly realistic features. This project partly funded development of the POPF planner, which is capable of solving a rich class of continuous temporal and numeric planning problems. POPF is being tested for use by a key industrial collaborator.
First Year Of Impact 2013
Sector Aerospace, Defence and Marine,Energy,Other
Impact Types Economic