The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

AI planning techniques are beginning to find use in a number of practical planning domains. However, the backward-chaining and partial-order-planning control strategies traditionally used in AI planning systems are not necessarily the best ones to use for practical planning problems. In this paper, we discuss some of the difficulties that can result from the use of backward chaining and partial-order planning, and we describe how these difficulties can be overcome by adapting Hierarchical Task-Network (HTN) planning to use a total-order control strategy that generates the steps of a plan in the same order that those steps will be executed. We also examine how introducing the total-order restriction into HTN planning affects its expressive power, and propose a way to relax the total-order restriction to increase its expressive power and range of applicability.

In the 40 years since Arthur Samuel's 1960 Advances in Computers chapter, enormous progress has been made in developing programs to play games of skill at a level comparable to, and in some cases beyond, what the best humans can achieve. In Samuel's time, it would have seemed unlikely that only a scant 40 years would be needed to develop programs that play world-class backgammon, checkers, chess, Othello, and Scrabble. These remarkable achievements are the result of a better understanding of the problems being solved, major algorithmic insights, and tremendous advances in hardware technology. Computer games research is one of the major success stories of articial intelligence. This chapter can be viewed as a successor to Samuel's work. A review of the scientic advances made in developing computer games is given. These ideas are the ingredients required for a successful program. Case studies for the games of backgammon, bridge, checkers, chess, Othello, poker, and Scrabb...

Awari is a two-player game of perfect information, played using 12 "pits" and 48 seeds or stones. The aim is for one player to capture more than half the seeds. In this work we show how an awari player can be evolved using a co-evolutionary approach where computer players play against one another, with the strongest players surviving and being mutated using an evolutionary strategy (ES). The players are represented using a simple evaluation function, representing the current game state, with each term of the function having a weight which is evolved using the ES. The output of the evaluation function is used in a mini-max search. We play the best evolved player against one of the strongest shareware programs (Awale) and are able to defeat the program at three of its four levels of play.

We propose the use of hierarchical (HTN) planning techniques to encode strategies that one or more Bots should execute while acting in highly dynamic environments such as Unreal Tournament © games. Our approach allows the formulation of a grand strategy but retains the ability of Bots to react to the events in the environment while contributing to the grand strategy.

High-level symbolic representations of actions to control the working of autonomous robots are used in all hybrid (reactive and deliberative) robot control architectures. Abstract action representations serve several purposes, such as structuring the control code, optimizing the robot performance, and providing a basis for reasoning about future robot action.