Route robustness is therefore a Vehicle route planning is a popular application of AI automated measure against the risk that the solution may not be executed planning methods. In numerous applications it is according to the a priori plan. The main idea behind supported with GPS navigation. Based on a generalized the concept of a robust plan is that such a plan should consist shortest-path approach it uses a directed graph as the search of numerous alternative plans, represented in a concise way, domain and edge weights set to match the required optimality and enable switching from the plan currently being executed criteria. Moreover, various additional constraints and to a new one as often as may become necessary. The degree heuristic information can be explored. (Nau, Ghallab, and of robustness is a qualitative factor referring to numerous Traverso 2004)

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.

In version 2.0, IBM ILOG CP Optimizer has been extended by the introduction of scheduling support based on the concept of optional interval variables. This paper formally describes the new modeling language features available to the users of CP Optimizer for resource-based scheduling. We show that the new language is flexible enough to model problems never before addressed by CP scheduling engines, as well as naturally describing classical scheduling problems found in the literature. This modeling power is based on a small number of general concepts such as intervals, sequences and functions. This makes the modeling language simple, clear and easy to learn, while maintaining the high-level structural aspects of the scheduling model.

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.

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.

Computer Science has a bad reputation among non-CS majors. This paper describes three assignments from a gentle introduction to programming course for non-majors that uses robots and simple game programming as a hook to get students interested in the subject. In each of the assignments presented, what might be considered a trivial twist to an instructor was a key factor in making an otherwise standard project into something that is more engaging.

We present a little crawling robot with a two DOF arm that learns to move forward within about 15 seconds in real time. Due to its small size and weight the robot is ideally suited for classroom demonstrations as well as for talks to the public. Students who want to practice their knowledge about reinforcement learning and value iteration can use a wireless connection to a PC and monitor the internal state of the robot such as the value function or the reward table. Due to its adaptivity, depending on the surface properties of the underground the robot may surprise its audience with unexpected but efficient walking policies. The GUI is open source and the robot hardware is available as a kit from the authors.

Qualitative techniques for spatial reasoning are important in artificial intelligence. We present an extended cardinal direction calculus (XCDC) for spatio-temporal event representation and reasoning. The methods presented in this paper can be used in systems based on natural language processing which are also discussed in this paper.

We propose a new approach for Temporal Diagnosis Problems. This approach is an extension of  Bouzid and Ligeza's  method for temporal diagnosis problems. In this latter work, the authors define a Temporal Causal Graph (TCG) where time delays are expressed as temporal instants. We extend the TCG by including two quantitative relations in order to handle temporal intervals. We call ExTCG this new model. Solving a temporal diagnosis problem represented by the ExTCG consists of finding all possible explanations. It is performed using a backtrack search algorithm. In many diagnosis applications, the generation of all possible explanations is not necessary. For this reason, we augment the ExTCG in order to consider the degree of causality between symptoms. We call weighted ExTCG this extended model. Solving it consists of finding the explanation  having the highest probability to occur. Through a real world diagnosis application in medicine, we illustrate the weighted ExTCG and its corresponding solving algorithm.