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.

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.

Planning has belonged to fundamental areas of AI since its beginning and sessions on planning are an integral part of major AI conferences. By generating activities necessary to achieve some goal, planning is also closely related to scheduling that deals with allocation of activities to scarce resources. Although the planning and scheduling communities are somehow separated, both areas have interacted more and more in recent years, especially when dealing with real-life problems. This FLAIRS special track attempts to make the conference attractive for the planning community, a traditional part of the AI family, and also the scheduling community -- especially for those using AImotivated solving techniques such as constraint satisfaction. FLAIRS 2008 hosted the first special track on AI planning and scheduling.

We propose a memory-based approach to the problem of goal-schema recognition. We use a generic episodic memory module to perform incremental goal schema recognition and to build the plan library. Unlike other case-based plan recognizers it does not require complete knowledge of the planning domain or the ability to record intermediate planning states. Similarity of plans is computed incrementally using a semantic matcher that considers the type and parameters of the observed actions.  We evaluate this approach on two datasets and show that it is able to achieve similar or better performance compared to a statistical approach, but offers important advantages: plan library is acquired incrementally and the memory structure it builds is multi-functional and can be used for other tasks such as plan generation or classification.

The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed in-field for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.