The theme of this workshop is encapsulated by a keynote given by Mark Musen, MD: “Artificial Intelligence in Medicine: It's Back to the Future.” In his talk, Dr. Musen will summarize the 30-year history of artificial intelligence in medicine including its successes and failures, and discuss current challenges and opportunities that have been made possible by recent technological and organizational changes. The rest of the program is structured along two main tracks that showcase applications of AI techniques and methods in health informatics and that present new theoretical and methodological developments on the boundary of AI and medicine.

List of organizers of the Expanding the Boundaries of Health Informatics Using Artificial Intelligence AAAI 2013 workshop.

Some of the most successful Poker agents that participate in the Annual Computer Poker Competition (ACPC) use an almost zero regret strategy: a strategy that approximates a Nash Equilibrium. However, it is still unfeasible to efficiently compute a Nash Equilibrium without some sort of information set abstraction due to the size of Poker’s search tree. One popular technique for abstracting Poker information sets is to group hands with similar Expected Hand Strength ( E [ HS ]) and thus play them in the same way. For large Poker variants, algorithms like CFR might need to calculate E [ HS ] billions of times, when the game abstraction is so large that it cannot be pre-computed, implying that E [ HS ] must be determined online. This way, improving the efficiency of this method would certainly reduce the computation time needed by CFR for these cases. In this paper we describe Average Rank Strength; a technique based on a pre-computed lookup table that speeds up E [ HS ] computation. Ours results demonstrate speed improvements of about three orders of magnitude and negligible results difference, when compared to the original E [ HS ].

Poker is increasingly becoming an area of interest in AI research, partly because of the complex qualities it exhibits which are absent from more traditionally studied games, such as chess. One of the most difficult but also most important aspects of poker is the need to infer information about your opponent while also handling his attempts at disinformation. This problem of ``opponent modelling" is a central aspect of poker agent design and has been approached in many different ways. In this paper we focus on one subset of the opponent modelling problem, namely that of bluff detection. We explore the effectiveness of different feature sets towards this task and test the ease with which the bluffs of various poker agents can be detected.

Slumbot NL is a heads-up no-limit hold'em poker bot built with a distributed disk-based implementation of counterfactual regret minimization (CFR). Our implementation enables us to solve a large abstraction on commodity hardware in a cost-effective fashion. A variant of the Public Chance Sampling (PCS) version of CFR is employed which works particularly well with our architecture.

Opponents are characterized by a Bayesian network intended to guide Monte-Carlo Tree Search through the game tree of No-Limit Texas Hold'em Poker. By using a probabilistic model of opponents, the network is able to integrate all available sources of information, including the infrequent revelations of hidden beliefs. These revelations are biased, and as such are difficult to incorporate into action prediction. The proposed network mitigates this bias via the expectation maximization algorithm and a probabilistic characterization of the hidden variables that generate observations. 

In poker, players tend to play sub-optimally due to theuncertainty in the game. Payoffs can be maximized byexploiting these sub-optimal tendencies. One way of realizingthis is to acquire the opponent strategy by recognizingthe key patterns in its style of play. Existing studieson opponent modeling in poker aim at predicting opponent’sfuture actions or estimating opponent’s hand.In this study, we propose a machine learning methodfor acquiring the opponent’s behavior for the purpose ofpredicting opponent’s future actions.We derived a numberof features to be used in modeling opponent’s strategy.Then, an ensemble learning method is proposed forgeneralizing the model. The proposed approach is testedon a set of test scenarios and shown to be effective.

List of organizers of the Computer Poker and Imperfect Information AAAI 2013 workshop.

We propose here an approach for learning parameters in Bayesian networks from incomplete datasets that are subject to equivalence constraints. These equivalence constraints arise from datasets where examples are tied together, in that we may not know the value of a particular variable, but whatever that value is, we know it must be the same across different examples. We formalize the problem by defining the notion of a constrained dataset — a dataset with equivalence constraints — and a corresponding constrained likelihood that we seek to optimize. We derive an EM algorithm to estimate parameters from constrained datasets, which reduces to the vanilla EM algorithm when estimating parameters from traditional datasets. Finally, we evaluate our general approach in clustering problems from semi-supervised learning, showing that it is competitive with more specialized approaches.

Exploring the conformational energy landscape of a molecule is an important but challenging problem because of the inherent complexity of this landscape. As part of this theme, various methods have been developed to compute transition paths between stable states of a molecule. Besides the methods classically used in biophysics/biochemistry, a recent approach originating from the robotics community has proven to be an efficient tool for conformational exploration. This approach, called the Transition-based RRT (T-RRT) is based on the combination of an effective path planning algorithm (RRT) with a Monte-Carlo-like transition test. In this paper, we propose an extension to TRRT based on a multi-tree approach, which we call Multi-T-RRT. It builds several trees rooted at different interesting points of the energy landscape and allows to quickly gain knowledge about possible conformational transition paths. We demonstrate this on the alanine dipeptide.