The leading approach to solving large imperfect information games is to pre-calculate an approximate solution using a simplified abstraction of the full game; that solution is then used to play the original, full-scale game. The abstraction step is necessitated by the size of the game tree. However, as the original game progresses, the remaining portion of the tree (the subgame) becomes smaller. An appealing idea is to use the simplified abstraction to play the early parts of the game and then, once the subgame becomes tractable, to calculate a solution using a finer-grained abstraction in real time, creating a combined final strategy. While this approach is straightforward for perfect information games, it is a much more complex problem for imperfect information games. If the subgame is solved locally, the opponent can alter his play in prior to this subgame to exploit our combined strategy. To prevent this, we introduce the notion of subgame margin, a simple value with appealing properties. If any best response reaches the subgame, the improvement of exploitability of the combined strategy is (at least) proportional to the subgame margin. This motivates subgame refinements resulting in large positive margins. Unfortunately, current techniques either neglect subgame margin (potentially leading to a large negative subgame margin and drastically more exploitable strategies), or guarantee only non-negative subgame margin (possibly producing the original, unrefined strategy, even if much stronger strategies are possible). Our technique remedies this problem by maximizing the subgame margin and is guaranteed to find the optimal solution. We evaluate our technique using one of the top participants of the AAAI-14 Computer Poker Competition, the leading playground for agents in imperfect information setting

In many multiagent environments, a designer has some, but limited control over the game being played. In this paper, we formalize this by considering incompletely specified games, in which some entries of the payoff matrices can be chosen from a specified set. We show that it is NP-hard for the designer to make this choices optimally, even in zero-sum games. In fact, it is already intractable to decide whether a given action is (potentially or necessarily) played in equilibrium. We also consider incompletely specified symmetric games in which all completions are required to be symmetric. Here, hardness holds even in weak tournament games (symmetric zero-sum games whose entries are all -1, 0, or 1) and in tournament games (symmetric zero-sum games whose non-diagonal entries are all -1 or 1). The latter result settles the complexity of the possible and necessary winner problems for a social-choice-theoretic solution concept known as the bipartisan set. We finally give a mixed-integer linear programming formulation for weak tournament games and evaluate it experimentally.

Poker is a family of card games that includes many varia- tions. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representa- tion. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold’em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competi- tive player against human experts. The contributions of this paper include: (1) a novel represen- tation for poker games, extendable to different poker vari- ations, (2) a Convolutional Neural Network (CNN) based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that signif- icantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.

Agent-Oriented Software Engineering (AOSE) is an emerging software engineering paradigm that advocates the application of best practices in the development of Multi-Agent Systems (MAS) through the use of agents and organizations of agents. This paper outlines the MADE system, which provides an interactive platform for people who are not well-versed in AOSE to contribute to the rapid prototyping of MASs with ease.

As a part of the workshop on Distributed and Multiagent Planning (DMAP) at the International Conference on Automated Planning and Scheduling (ICAPS) 2015, we have organized a competition in distributed and multiagent planning. The main aims of the competition were to consolidate the planners in terms of input format; to promote development of multiagent planners both inside and outside of the multiagent research community; and to provide a proof-of-concept of a potential future multiagent planning track of the International Planning Competition (IPC). In this paper we summarize course and highlights of the competition.

The Angry Birds AI Competition (aibirds.org) has been held annually since 2012 in conjunction with some of the major AI conferences, most recently with IJCAI 2015. The goal of the competition is to build AI agents that can play new Angry Birds levels as good as or better than the best human players. Successful agents should be able to quickly analyze new levels and to predict physical consequences of possible actions in order to select actions that solve a given level with a high score. Agents have no access to the game internal physics, but only receive screenshots of the live game. In this paper we describe why this problem is a challenge for AI, and why it is an important step towards building AI that can successfully interact with the real world. We also summarise some highlights of past competitions, including a new competition track we introduced recently.

My thesis addresses the problem of planning under teammate behavior uncertainty by introducing the concept of intentional multiagent communication within ad hoc teams. In partially observable multiagent domains, agents much share information regarding aspects of the environment such that uncertainty is reduced across the team, permitting better coordination. Similarly, we consider how communication may be utilized within ad hoc teams to resolve behavioral uncertainty. Transmitting intentional messages allows agents to adjust predictions of a teammate's individual course of action. In short, an ad hoc agent coordinating with an unknown teammate can identify uncertainties within its own predictive model of teammate behavior then request the appropriate policy information, allowing the agent to adapt its personal plan. The main contribution of this work is the characterization of the interaction between learning, communication, and planning in ad hoc teams.

In online social networks (OSNs), users are allowed to create and share content about themselves and others. When multiple entities start distributing content, information can reach unintended individuals and inference can reveal more information about the user. Existing applications do not focus on detecting privacy violations before they occur in the system. This thesis proposes an agent-based representation of a social network, where the agents manage users' privacy requirements and create privacy agreements with agents. The privacy context, such as the relations among users, various content types in the system, and so on are represented with a formal language. By reasoning with this formal language, an agent checks the current state of the system to resolve privacy violations before they occur. We argue that commonsense reasoning could be useful to solve some of privacy examples reported in the literature. We will develop new methods to automatically identify private information using commonsense reasoning, which has never been applied to privacy context. Moreover, agents may have conflicting privacy requirements. We will study how to use agreement technologies in privacy settings for agents to resolve conflicts automatically.

Autonomous systems must consider the moral ramifications of their actions. Moral norms vary among people and depend on common sense, posing a challenge for encoding them explicitly in a system. I propose to develop a model of repeated analogical chaining and analogical reasoning to enable autonomous agents to interactively learn to apply common sense and model an individual’s moral norms.

In impromptu or ad hoc settings, participating players are precluded from precoordination. Subsequently, each player's own model is private and includes some uncertainty about the others' types or behaviors. Harsanyi's formulation of a Bayesian game lays emphasis on this uncertainty while the players each play exactly one turn. We propose a new game-theoretic framework where Bayesian players engage in a Markov game and each has private but imperfect information regarding other players' types. Consequently, we construct player types whose structure is explicit and includes a finite level belief hierarchy instead of utilizing Harsanyi's abstract types and a common prior distribution. We formalize this new framework and demonstrate its effectiveness on two standard ad hoc teamwork domains involving two or more ad hoc players.