Adaptive strategies are popular in poker AI research. Three desirable properties of an adaptive poker bot are optimality, generality and speed of response to new opponents. These three properties though cannot be achieved simultaneously for most imperfect information games; some trade-off must be made between them. This general principle is connected to recent work on poker AI and particularly the total bankroll competitions at the Annual Computer Poker Competition (ACPC). This paper is meant to generate discussion between researchers about how to explain and quantify these trade-offs better and to possible future directions for research.

This paper is devoted to fair optimization in Multiobjective Markov Decision Processes (MOMDPs). A MOMDP is an extension of the MDP model for planning under uncertainty while trying to optimize several reward functions simultaneously. This applies to multiagent problems when rewards define individual utility functions, or in multicriteria problems when rewards refer to different features. In this setting, we study the determination of policies leading to Lorenz-non-dominated tradeoffs. Lorenz dominance is a refinement of Pareto dominance that was introduced in Social Choice for the measurement of inequalities. In this paper, we introduce methods to efficiently approximate the sets of Lorenz-non-dominated solutions of infinite-horizon, discounted MOMDPs. The approximations are polynomial-sized subsets of those solutions.

In this paper we provide an algorithm for utility maximization of a movie supplier service, in two different settings, one with prices and the other without. This algorithm is provided along with an extensive experiment demonstrating its performance. We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.

In this paper, we show how to model the matching problem as a problem of joint inference. In opposite to existing ap-proaches, we distinguish between the layer of labels and the layer of concepts and properties. Entities from both layers appear as first class citizens in our model. We present an ex-ample and explain the benefits of our approach. Moreover, we argue that our approach can be extended to generate cor-respondences involving complex concept descriptions.

We describe a game environment we have developed that we call the Strategy Engine for Programming Intelligent Agents (SEPIA). SEPIA is based on real-time strategy games, but modified extensively to preferentially support the development of artificial agents rather than human play. Through flexible configuration options, SEPIA is designed to be pedagogically scalable: suitable for use at the undergraduate and graduate levels, and also as a research testbed. We also describe assignments and our experiences with this environment in undergraduate and graduate classes.

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for "big data" over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. It has been shown that inference and training can indeed benefit from exploiting symmetries, for example by lifting loopy belief propagation (LBP).% can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified LBP running on the lifted graph yields the same marginals as LBP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able and thus combine the two orthogonal approaches to scaling inference, namely exploiting symmetries and employing parallel computations.

Verbal tasks that have traditionally been difficult for computer systems but are easy for young children are among AI’s “grand challenges”. We present the results of testing the ConceptNet 4 system on the verbal part of the standard WPPSI-III IQ test, using simple test-answering algorithms. It is found that the system has the Verbal IQ of an average four-year-old child.

Domains such as vision, bioinformatics, web search and web rankings involve datasets where number of features is very large. Feature selection is commonly employed to deal with high dimensional data. Recently, Quadratic Programming Feature Selection (QPFS) has been shown to outperform many of the existing feature selection methods for a variety of datasets. In this paper, we propose a Sequential Minimal Optimization (SMO) based framework for QPFS. This helps in reducing the cubic computational time (in terms of data dimension) of the standard QPFS to quadratic time in practice. Further, our approach has significantly less memory requirement than QPFS. This memory saving can be critical for doing feature selection in high dimensions. The performance of our approach is demonstrated using three publicly available benchmark datasets from bioinformatics domain.

Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans in a safe and natural way to accomplish tasks cooperatively. Robots with variable impedance capabilities opens the door to new challenging applications, where the learning algorithms must be extended by encapsulating force and vision information. In this paper we propose a framework to transfer impedance-based behaviors to a torque-controlled robot by kinesthetic teaching. The proposed model encodes the examples as a task-parameterized statistical dynamical system, where the robot impedance is shaped by estimating virtual stiffness matrices from the set of demonstrations. A collaborative assembly task is used as testbed. The results show that the model can be used to modify the robot impedance along task execution to facilitate the collaboration, by triggering stiff and compliant behaviors in an on-line manner to adapt to the user's actions.

The increased demand for distributed computations on “big data” has led to solutions such as SCOPE, DryadLINQ, Pig, and Hive, which allow the user to specify queries in an SQL-like language, enriched with sets of user-defined operators. The lack of exact semantics for user-defined operators interferes with the query optimization process, thus putting the burden of suggesting, at least partial, query plans on the user. In an attempt to ease this burden, we propose a formal model that allows for data-parallel program synthesis (DPPS) in a semantically well-defined manner. We show that this model generalizes existing frameworks for data-parallel computation, while providing the flexibility of query plan generation that is currently absent from these frameworks. In particular, we show how existing, off-the-shelf, AI planning tools can be used for solving DPPS tasks.