Ms Pac-Man versus Ghost Team CEC 2011 competition

Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE

Artful Good Faith: An Essay on Law, Custom, and Intermediaries in Art Markets

This Essay explores relationships between custom and law in the United States in the context of markets for art objects. The Essay argues that these relationships are dynamic, not static, and that law can prompt evolution in customary practice well beyond the law\u27s formal requirements. Understanding these relationships in the context of art markets requires due attention to two components distinctive to art markets: the role of dealers and auction houses as transactional intermediaries as well as the role of museums as end-collectors. In the last decade, the business practices of major transactional intermediaries reflected a significant shift in customary practice, with attention newly focused on the provenance (ownership history) of objects consigned for sale and on long-standing concerns with an object\u27s condition and authorship. During the same time major museums developed new policies and practices applicable to new acquisitions and objects already in held in collections, focused in particular on archaeological objects and ancient art, as well as paintings present in European countries subject to the Nazi regime between 1932 and 1945. The Essay argues that, in both cases, law furnished the backdrop to significant shifts in customary practice, augmented by heightened public knowledge and concern. Custom evolved in response to salient episodes of enforcement of the law, which furnished further rallying points for newly broadened or awakened public interest and concern. The relationships explored in this Essay are relevant to ongoing debate about the merits of the underlying law. In the United States, it has long been true that nemo dat quod non habet—no one can give what one does not have—with the consequence that a thief cannot convey good title. The subsequent transferees lack good title and are not insulated against claims by the rightful owner even when the transferees acted in good faith. To be sure, an elapsed statute of limitations may furnish a defense, as may the equitable doctrine of laches. Prior scholarship notes that the United States is unusual, but not unique, because it does not recognize any good-faith purchaser defense in this context and because it does not require that the rightful owner of a stolen object compensate the good-faith purchaser as a condition of obtaining the return of the object. However, this scholarship does not acknowledge (or does not emphasize) the significance of transactional intermediaries within art markets or the operation of customary practices of museums and transactional intermediaries. This Essay thus adds the context requisite to evaluating the merits of the relevant law

Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information

In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.Comment: Submitted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP

Automated Video Game Testing Using Synthetic and Human-Like Agents

In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGPIRL captures multiple policies executed by human testers. These testers' aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance

An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits

In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards. Low amounts of policy information favor the exploitation of existing knowledge. Information, in this criterion, is quantified by a parameter that can be varied during search. We demonstrate that a simulated-annealing-like update of this parameter, with a sufficiently fast cooling schedule, leads to an optimal regret that is logarithmic with respect to the number of episodes.Comment: Entrop