This article introduces a reflexion about behavioural specification for interactive and participative agent-based simulation in virtual reality. Within this context, it is neces sary to reach a high level of expressivness in order to enforce interactions between the designer and the behavioural model during the in-line prototyping. This requires to consider the need of semantic very early in the design process. The Intentional agent model is here exposed as a possible answer. It relies on a mixed imperative and declarative approach which focuses on the link between decision and action. The design of a tool able to simulate virtual environment implying agents based on this model is discuss

Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? In this paper, we address the problem of identifying social groups from conversation data and present results of an empirical study on identifying groups in a virtual world. Virtual worlds are interesting because group membership is more shaped by common interests and less influenced by cultural and socio-economic factors. Our finding is that a combination of network measures is more predictive of group membership than language cues, and that both types of features can be combined to improve prediction.

Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite the construction and deployment of intelligent systems. In practice however, there are many barriers to achieving this goal. In this article, we present a prototype system for the real-world context of transferring knowledge of American football from video observation to control in a game simulator. We trace an example play from the raw video through execution and adaptation in the simulator, highlighting the system's component algorithms along with issues of complexity, generality, and scale. We then conclude with a discussion of the implications of this work for other applications, along with several possible improvements.

In the Canadian Traveler Problem (CTP) a traveling agent is given a weighted graph, where some of the edges may be blocked, with a known probability. The agent needs to travel to a given goal. A solution for CTP is a policy, that has the smallest expected traversal cost. CTP is intractable. Previous work has focused on the case of a single agent. We generalize CTP to a repeated task version where a number of agents need to travel to the same goal, minimizing their combined travel cost. We provide optimal algorithms for the special case of disjoint path graphs. Based on a previous UCT-based approach for the single agent case, a framework is developed for the multi-agent case and four variants are given - two of which are based on the results for disjoint-path graphs. Empirical results show the benefits of the suggested framework and the resulting heuristics. For small graphs where we could compare to optimal policies, our approach achieves near optimal results at only a fraction of the computation cost.

Scaling up the number of simultaneously moving units in pathfinding problems to hundreds, or even thousands, is well beyond the capability of theoretically optimal algorithms in practice, which is consistent with existing intractability results. Significant scalability can be achieved by trading off solution optimality, which motivates evaluating the quality of suboptimal solutions, especially in instances much larger than can be handled by optimal algorithms. We consider pathfinding in uniform cost grid maps, and we study the solution quality using the three most common quality criteria, total travel distance , sum of actions , and makespan . We focus on MAPP, which has been shown as state-of-the-art in terms of scalability and success ratio (i.e., percentage of solved units) on realistic game grid maps. Until now, the quality of MAPP's solutions had not been as extensively analyzed. We introduced enhancements that significantly improve MAPP's solution quality. For example, its sum of actions is cut to half on average. MAPP becomes competitive in terms of solution quality with FAR and WHCA*, two successful algorithms from the literature. To evaluate the quality of suboptimal solutions in instances beyond the capability of optimal algorithms, we use lower bounds of optimal values to show our solutions have a reasonable quality. For example, MAPP's average total travel distance is 19 percent longer than the lower bound.

We address the problem of optimal path finding for multiple agents where agents must not collide and their total travel cost should be minimized. Previous work used traditional single-agent search variants of the A* algorithm. In Sharon et. al. (2011), we introduced a novel two-level search algorithm framework for this problem. The high-level searches a novel search tree called increasing cost tree (ICT). The low-level performs a goal test on each ICT node. The new framework, called ICT search (ICTS), showed to run faster than the previous state-of-the-art A* approach by up to three orders of magnitude in many cases. In this paper we focus on the low-level of ICTS which performs the goal test. We introduce a number of optional pruning techniques that can significantly speed up the goal test. We discuss these pruning techniques and provide supporting experimental results.

Multi-agent pathfinding, where multiple agents must travel to their goal locations without getting stuck, has been studied in both theoretical and practical contexts, with a variety of both optimal and sub-optimal algorithms proposed for solving problems. Recent work has shown that there is a linear-time check for whether a multi-agent pathfinding problem can be solved in a tree, however this was not used to actually produce solutions. In this paper we provide a constructive proof of how to solve multi-agent pathfinding problems in a tree that culminates in a novel approach that we call the tree-based agent swapping strategy (TASS). Experimental results showed that TASS can find solutions to the multi-agent pathfinding problem on a highly crowded tree with 1000 nodes and 996 agents in less than 8 seconds. These results are far more efficient and general than existing work, suggesting that TASS is a productive line of study for multi-agent pathfinding.

Decentralized control of cooperative systems captures the operation of a group of decision makers that share a single global objective. The difficulty in solving optimally such problems arises when the agents lack full observability of the global state of the system when they operate. The general problem has been shown to be NEXP-complete. In this paper, we identify classes of decentralized control problems whose complexity ranges between NEXP and P. In particular, we study problems characterized by independent transitions, independent observations, and goal-oriented objective functions. Two algorithms are shown to solve optimally useful classes of goal-oriented decentralized processes in polynomial time. This paper also studies information sharing among the decision-makers, which can improve their performance. We distinguish between three ways in which agents can exchange information: indirect communication, direct communication and sharing state features that are not controlled by the agents. Our analysis shows that for every class of problems we consider, introducing direct or indirect communication does not change the worst-case complexity. The results provide a better understanding of the complexity of decentralized control problems that arise in practice and facilitate the development of planning algorithms for these problems.

Supply chain formation is the process of determining the structure and terms of exchange relationships to enable a multilevel, multiagent production activity. We present a simple model of supply chains, highlighting two characteristic features: hierarchical subtask decomposition, and resource contention. To decentralize the formation process, we introduce a market price system over the resources produced along the chain. In a competitive equilibrium for this system, agents choose locally optimal allocations with respect to prices, and outcomes are optimal overall. To determine prices, we define a market protocol based on distributed, progressive auctions, and myopic, non-strategic agent bidding policies. In the presence of resource contention, this protocol produces better solutions than the greedy protocols common in the artificial intelligence and multiagent systems literature. The protocol often converges to high-value supply chains, and when competitive equilibria exist, typically to approximate competitive equilibria. However, complementarities in agent production technologies can cause the protocol to wastefully allocate inputs to agents that do not produce their outputs. A subsequent decommitment phase recovers a significant fraction of the lost surplus.

We address the problem of finding the shortest path between two points in an unknown real physical environment, where a traveling agent must move around in the environment to explore unknown territory. We introduce the Physical-A* algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes that A* would expand and returns the shortest path between the two points. However, due to the physical nature of the problem, the complexity of the algorithm is measured by the traveling effort of the moving agent and not by the number of generated nodes, as in standard A*. PHA* is presented as a two-level algorithm, such that its high level, A*, chooses the next node to be expanded and its low level directs the agent to that node in order to explore it. We present a number of variations for both the high-level and low-level procedures and evaluate their performance theoretically and experimentally. We show that the travel cost of our best variation is fairly close to the optimal travel cost, assuming that the mandatory nodes of A* are known in advance. We then generalize our algorithm to the multi-agent case, where a number of cooperative agents are designed to solve the problem. Specifically, we provide an experimental implementation for such a system. It should be noted that the problem addressed here is not a navigation problem, but rather a problem of finding the shortest path between two points for future usage.