In this paper, we investigate the hypothesis that plan recognition can significantly improve the performance of a case-based reinforcement learner in an adversarial action selection task. Our environment is a simplification of an American football game. The performance task is to control the behavior of a quarterback in a pass play, where the goal is to maximize yardage gained. Plan recognition focuses on predicting the play of the defensive team. We modeled plan recognition as an unsupervised learning task, and conducted a lesion study. We found that plan recognition was accurate, and that it significantly improved performance. More generally, our studies show that plan recognition reduced the dimensionality of the state space, which allowed learning to be conducted more effectively. We describe the algorithms, explain the reasons for performance improvement, and also describe a further empirical comparison that highlights the utility of plan recognition for this task.

In our experiments with four well-known systems for solving partially observable planning problems  (Contingent-FF, MBP, PKS, and POND), we were greatly surprised to find that they could only solve problems with a small number of contingencies. Apparently they were repeatedly trying to solve many combinations of contingencies at once, thus unnecessarily using up huge amounts of time and space. This difficulty can be alleviated if the planner can maintain focus on the contingency that it is currently trying to solve. We provide a way to accomplish this by incorporating focusing information directly into the planning domain's operators, without any need to modify the planning algorithm itself. This enables the above planners to solve larger problems and to solve them much more quickly. We also provide a new planner, FOCUS, in which focusing information can be provided as a separate input. This provides even better performance by allowing the planner to utilize more extensive focusing information.

In this paper an application of the metaheuristic Ant Colony Optimization to optimal planning is presented. It is well known that finding out optimal solutions to planning problem is a very hard computational problem. Approximate methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good solutions, often close to optimal ones. Since one of the most performing stochastic method for combinatorial optimization is ACO, we have decided to use this technique to design an algorithm which optimizes plan length in propositional planning. This algorithm has been implemented and some empirical evaluations have been performed. The results obtained are encouraging and show the feasibility of this approach.

Generating a schedule for a professional sports league is an extremely demanding task. Good schedules have many benefits for the league, for example higher incomes, lower costs and more interesting and fairer seasons. This paper presents a successful solution method to schedule the Finnish 1st division ice hockey league. The solution method is an improved version of the method used to schedule the Finnish major ice hockey league. The method is a combination of local search heuristics and evolutionary methods. An analyzer for the quality of the produced schedules will be introduced. Finally, we propose a set of test instances that we hope the researchers of the sports scheduling problems would adopt. The generated schedule for the Finnish 1st division ice hockey league is currently in use for the season 2008-2009.

Planning problems deal with finding a sequence of actions that transfer the initial state of the world into a desired state. Frequently such problems are solved by dedicated algorithms but there exist planners based on translating the planning problem into a different formalism such as constraint satisfaction or Boolean satisfiability and using a general solver for this formalism. The paper describes how to enhance existing constraint models of planning problems by using techniques such as symmetry breaking (dominance rules), singleton consistency, and lifting.

A problem of path planning for multiple robots is addressed in this paper. A specific case of the problem with so called theta-like environment is studied. This case of the problem represent structurally the simplest solvable case and an eventual solving method for this case can be used as a building block for more general solving procedures. We propose a solving method for multi-robot path planning in theta-like environments that constructs a solution by composing it of the pre-calculated shortest solutions of certain sub-problems. This approach prefers short overall solutions. Moreover, we propose a new algorithm for pre-calculating shortest solutions of sub-problems - it is in fact an improvement of the IDA* algorithm. An experimental comparison of our methods with existing techniques is presented in the paper.

The Fourth International Conference on Knowledge Capture was held October 28-31, 2007 in Whistler, British Columbia. K-CAP 2007 included two invited talks, technical papers, posters, and demonstrations. Topics included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixed-initiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice taking systems.

SPARK'08 was the first edition of a workshop series designed to provide a stable, long-term forum where researchers could discuss the applications of planning and scheduling techniques to real problems. Animated discussion characterized the workshop, which was collocated with Eighteenth International Conference on Automated Planning and Scheduling (ICAPS-08) held in Sydney, Australia in September 2008.

SPARK'08 was the first edition of a workshop series designed to provide a stable, longterm forum where researchers could discuss Workshop (SPARK) was established to help address this issue. Building on precursory events, SPARK'08 was the first workshop designed Scheduling (ICAPS-08) held in Sydney, Australia, in September 2008. Like its immediate predecessor (the ICAPS'07 Workshop on Moving Planning and Scheduling Systems), the 2008 SPARK workshop was collocated with the International Conference on Automated Planning and Scheduling (ICAPS), a premier forum for research in AI planning and scheduling, and the International Conference on Principles and Practice of Constraint Programming (CP). A handful of outstanding application-oriented papers are presented each year at the ICAPS conference. Time and again, in invited talks and in open microphone discussion sessions such as ICAPS's Festivus (where conference participants air their grievances in an open and entertaining way), researchers have lamented the small number of applications papers accepted at conferences such as ICAPS, CP, and the AAAI Conference on Artificial Intelligence.

The theme of this article is the dynamics of evolution of agents. That theme is applied to the evolution of constraint satisfaction, of agents themselves, of our models of agents, of artificial intelligence and, finally, of the Association for the Advancement of Artificial Intelligence (AAAI). The overall thesis is that constraint satisfaction is central to proactive and responsive intelligent behavior.