For problems such as pathfinding in video games and robotics, a search algorithm must be real-time (return the next move within a fixed time bound) and dynamic (accommodate edge costs that can increase and decrease before the goal is reached). Existing real-time search algorithms, such as LSS-LRTA*, can handle edge cost increases but do not handle edge cost decreases. Existing dynamic search algorithms, such as D* Lite, are not real-time. We show how these two families of algorithms can be combined using bidirectional search, producing Real-Time D* (RTD*), the first real-time search algorithm designed for dynamic worlds. Our empirical evaluation shows that, for dynamic grid pathfinding, RTD* results in significantly shorter trajectories than either LSS-LRTA* or naive real-time adaptations of D* Lite because of its ability to opportunistically exploit shortcuts.

Transposition tables are a well-known method for pruning duplicates in heuristic search. This paper presents a detailed analysis of transposition tables for IDA*. We show that some straightforward implementations of IDA* with transposition tables (IDA*+TT) can result in suboptimal solutions being returned. Furthermore, straightforward implementations of IDA*+TT are not complete. We identify several variants of IDA*+TT which are guaranteed to return the optimal solution, as well as a complete variant. An empirical study shows that IDA*+TT can significantly improve upon the performance of A* in domain-independent planning.

Memory-based heuristic techniques have been used to effectively reduce search times in implicit graphs. Recently, these techniques have been applied to improving search times in explicit graphs. This paper presents a new memory-based, additive heuristic that can be used on a type of explicit graph: the four-connected gridworld. The heuristic reduces the number of expanded nodes by up to five times, reduces execution time by up to 29 times, and can efficiently accommodate graph changes.

Welcome to the Third International Symposium on a long line of research that was carried out in the Combinatorial Search (SoCS)! This is an important past decade by him and others about the important year for SoCS as we have established archival proceedings topic of search in nondeterministic environments. These proceedings are the Finally, we scheduled an important panel discussion result of hard work by many, from researchers to reviewers about the differences and mutual influence of domain and the publisher. Every submitted search for planning environments. Wheeler Ruml paper was assigned to three anonymous reviewers; moderates the panel, which includes three experts all experts in the topic of the paper.

Constraint problems can be trivially solved in parallel by exploring different branches of the search tree concurrently. Previous approaches have focused on implementing this functionality in the solver, more or less transparently to the user. We propose a new approach, which modifies the constraint model of the problem. An existing model is split into new models with added constraints that partition the search space. Optionally, additional constraints are imposed that rule out the search already done. The advantages of our approach are that it can be implemented easily, computations can be stopped and restarted, moved to different machines and indeed solved on machines which are not able to communicate with each other at all.

Constraint solvers are complex pieces of software which require many design decisions to be made by the implementer based on limited information. These decisions affect the performance of the finished solver significantly [16]. Once a design decision has been made, it cannot easily be reversed, although a different decision may be more appropriate for a particular problem. We investigate using machine learning to make these decisions automatically depending on the problem to solve. We use the alldifferent constraint as a case study. Our system is capable of making nontrivial, multilevel decisions that improve over always making a default choice and can be implemented as part of a general-purpose constraint solver.

We follow knowledge engineering techniques and observe the expert when he analyses a social discussion forum. Then a number of models are defined. These models emphasises the process followed by the expert and a number of criteria used in his analysis. Results can be used as guides that help to understand and annotate discussion forum. We aim at modelling other pragmatics analysis in order to complete the base of guides; criteria, process, etc. of discussion analysis KEYWORDS:

The successful completion of a software development process depends on the analytical capability and foresightedness of the project manager. For the project manager, the main intriguing task is to manage the risk factors as they adversely influence the completion deadline. One such key risk factor is staff training. The risk of this factor can be avoided by pre-judging the amount of training required by the staff. So, a procedure is required to help the project manager make this decision. This paper presents a system that uses influence diagrams to implement the risk model to aid decision making. The system also considers the cost of conducting the training, based on various risk factors such as, (i) Lack of experience with project software; (ii) Newly appointed staff; (iii) Staff not well versed with the required quality standards; and (iv) Lack of experience with project environment. The system provides estimated requirement details for staff training at the beginning of a software development project.

Knowledge Management is a global process in companies. It includes all the processes that allow capitalization, sharing and evolution of the Knowledge Capital of the firm, generally recognized as a critical resource of the organization. Several approaches have been defined to capitalize knowledge but few of them study how to learn from this knowledge. We present in this paper an approach that helps to enhance learning from profession knowledge in an organisation. We apply our approach on knitting industry.

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Na\"ive Bayesian classifier (Na\"ive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.