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Many complex domains and even larger problems in simple domains remain challenging in spite of the recent progress in planning. Besides developing and improving planning technologies, re-engineering a domain by utilising acquired knowledge opens up a potential avenue for further research. Moreover, macro-actions, when added to the domain as additional(More)
One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made,(More)
In this paper, we present a Web-based demonstration of a Course of Action (COA) comparison matrix being used as an interface to an O-Plan plan server to explore multiple qualitatively different plan options. The scenario used for this demonstration is concerned with crisis operations on the island of Pacifica. The COA comparison matrix allows the user to(More)
As participants in this Dagstuhl session address the challenge of General Video Game Playing (GVGP), we have recognised the need to create a Video Game Description Language (VGDL). Unlike General Game Playing, we have envisioned GVGP will not require a prescribed language to facilitate understanding of the logic of the game: requiring the computational(More)
There are many different approaches to solving planning problems, one of which is the use of domain specific control knowledge to help guide a domain independent search algorithm. This paper presents L2Plan which represents this control knowledge as an ordered set of control rules, called a policy, and learns using genetic programming. The genetic program's(More)
—This paper compares supervised and unsupervised learning mechanisms for the emergence of cooperative multiagent spatial coordination using a top-down approach. By observing the global performance of a group of homogeneous agents—supported by a nonglobal knowledge of their environment—we attempt to extract information about the minimum size of the agent(More)
This paper describes Wizard, a generalised macro-learning method that participated in the Learning Track of the 6th International Planning Competition. Given a planner, a domain, and a few example problems, Wizard suggests macros that might help the planner solve future problems in the domain faster. This implementation compiles macros into regular actions(More)
Natural-language generation (NLG) techniques can be used to automatically produce technical documentation from a domain knowledge base and linguistic and contextual models. We discuss this application of NLG technology from both a technical and a usefulness (costs and benefits) perspective. This discussion is based largely on our experiences with the idas(More)
In this paper, we outline the requirements of a planning and decision aid to support US Army small unit operations in urban terrain and show how AI planning technologies can be exploited in that context. The work is a rare example of a comprehensive use of AI technologies across the whole planning lifecycle, set in a realistic application in which the(More)
The Intelligent Documentation Advisory System generates on-line documentation and help messages from a domain knowledge base, using natural-language (NL) generation techniques. This paper gives an overview of IDAS, with particular emphasis on: (1) its architecture and the types of questions it is capable of answering; (2) its KR and NL generation systems,(More)