Monte-Carlo search is successfully used in simulation-based planning for various large-scale sequential decision problems, and the UCT algorithm seems to be the choice in most (if not all) such recent success stories. Based on some recent discoveries in theory and empirical analysis of Monte-Carlo search, here we argue that, if online sequential decision making is your problem, and Monte-Carlo tree search is your way to go, then UCT is unlikely to be the best fit for your needs.

One typically proves infeasibility in satisfiability/constraint satisfaction (or optimality in integer programming) by constructing a tree certificate. However, deciding how to branch in the search tree is hard, and impacts search time drastically. We explore the power of a simple paradigm, that of throwing random darts into the assignment space and then using information gathered by that dart to guide what to do next. Such guidance is easy to incorporate into state-of-the-art solvers. This method seems to work well when the number of short certificates of infeasibility is moderate, suggesting the overhead of throwing darts can be countered by the information gained by these darts. We explore results supporting this suggestion both on instances from a new generator where the size and number of short certificates can be controlled, and on industral instances from the annual SAT competition.

In real-time domains such as video games, a planning algo- rithm has a strictly bounded time before it must return the next action for the agent to execute. We introduce a realistic video game benchmark domain that is useful for evaluating real-time heuristic search algorithms. Unlike previous bench- marks such as grid pathfinding and the sliding tile puzzle, this new domain includes dynamics and induces a directed graph. Using both the previous and new domains, we investigate sev- eral enhancements to a leading real-time search algorithm, LSS-LRTA*. We show experimentally that 1) it is not dif- ficult to outperform A* when optimizing goal achievement time, 2) it is better to plan after each action than to commit to multiple actions or to use a dynamically sized lookahead, 3) A*-based lookahead can cause undesirable actions to be selected, and 4) on-line de-biasing of the heuristic can lead to improved performance. We hope that this new domain and results will stimulate further research on applying real-time search to dynamic real-time domains.

Multi-agent pathfinding (MAPF) deals with planning paths for individual agents such that a global cost function (e.g., the sum of costs) is minimized while avoiding collisions between agents. Previous work proposed centralized or fully cooperative decentralized algorithms assuming that agents will follow paths assigned to them. When agents are {\em self-interested}, however, they are expected to follow a path only if they consider that path to be their most beneficial option. In this paper we propose the use of a taxation scheme to implicitly coordinate self-interested agents in MAPF. We propose several taxation schemes and compare them experimentally. We show that intelligent taxation schemes can result in a lower total cost than the non coordinated scheme even if we take into consideration both travel cost and the taxes paid by agents.

There has been a tremendous advance in domain-independent planning over the past decades, and planners have become increasingly efficient at finding plans. However, this has not been paired by any corresponding improvement in detecting unsolvable instances. Such instances are obviously important but largely neglected in planning. In other areas, such as constraint solving and model checking, much effort has been spent on devising methods for detecting unsolvability. We introduce a method for detecting unsolvable planning instances that is loosely based on consistency checking in constraint programming. Our method balances completeness against efficiency through a parameter k: the algorithm identifies more unsolvable instances but takes more time for increasing values of k. We present empirical data for our algorithm and some standard planners on a number of unsolvable instances, demonstrating that our method can be very efficient where the planners fail to detect unsolvability within reasonable resource bounds. We observe that planners based on the h^m heuristic or pattern databases are better than other planners for detecting unsolvability. This is not a coincidence since there are similarities (but also significant differences) between our algorithm and these two heuristic methods.

The k-medoids problem is a combinatorial optimisation problem with multiples applications in Resource Allocation, Mobile Computing, Sensor Networks and Telecommunications.Real instances of this problem involve hundreds of thousands of points and thousands of medoids.Despite the proliferation of parallel architectures, this problem has been mostly tackled using sequential approaches.In this paper, we study the impact of space-partitioning techniques on the performance of parallel local search algorithms to tackle the k-medoids clustering problem, and compare these results with the ones obtained using sampling.Our experiments suggest that approaches relying on partitioning scale more while preserving the quality of the solution.

Incremental heuristic searches reuse their previous search efforts to speed up the current search. Anytime search algorithms iteratively tune the solutions based on available search time. Anytime D* (AD*) is an incremental anytime search algorithm that combines these two approaches. AD* uses an inflated heuristic to produce bounded suboptimal solutions and improves the solution by iteratively decreasing the inflation factor. If the environment changes, AD* recomputes a new solution by propagating the new costs. Recently, a different approach to speed up replanning (TLPA*/TD* Lite) was proposed that relies on selective truncation of cost propagations instead of heuristic inflation. In this work, we present an algorithm called Anytime Truncated D* (ATD*) that combines heuristic inflation with truncation in an anytime fashion. We develop truncation rules that can work with an inflated heuristic without violating the completeness/suboptimality guarantees, and show how these rules can be applied in conjunction with heuristic inflation to iteratively refine the replanning solutions with minimal reexpansions. We explain ATD*, discuss its analytical properties and present experimental results for 2D and 3D (x, y, heading) path planning demonstrating its efficacy for anytime replanning.

Abstracts of the invited speaker talks Modeling, Global Constraints, and Decomposition by J. Christopher Beck and Applications of Graph Search in Group Theory and Proteomics by Gene Cooperman, presented that the 2013 SoCS Symposium.

Community-based question answering (CQA) services such as Yahoo! Answers have been widely used by Internet users to get the answers for their inquiries. The CQA services totally rely on the contributions by the users. However, it is known that newcomers are prone to lose their interests and leave the communities. Thus, finding expert users in an early phase when they are still active is essential to improve the chances of motivating them to contribute to the communities further. In this paper, we propose a novel approach to discovering "potentially" contributive users from recently-joined users in CQA services. The likelihood of becoming a contributive user is defined by the user's expertise as well as availability, which we call the answer affordance. The main technical difficulty lies in the fact that such recently-joined users do not have abundant information accumulated for many years. We utilize a user's productive vocabulary to mitigate the lack of available information since the vocabulary is the most fundamental element that reveals his/her knowledge. Extensive experiments were conducted with a huge data set of Naver Knowledge-In (KiN), which is the dominating CQA service in Korea. We demonstrate that the top rankers selected by the answer affordance outperformed those by KiN in terms of the amount of answering activity.

The Sixth Conference on Artificial held in cooperation with AAAI. Fifth Biannual Humaine Association ICWSM-13 will be held July 8-11, 2013 IJCAI-13 will be held August 3-Conference on Affective Computing at MIT, Cambridge, MA USA 9, 2013 in Beijing, China and Intelligent Interaction. IC3K 2013 will be held on Enterprise Information Systems. September 19-22, 2013 in Vilamoura, ICEIS 2013 will be held July 3-7, 2013 Algarve, Portugal Ninth AAAI Conference on Digital in Angers, France Entertainment. CI 2013 will be held September 20-22, Based Reasoning.