This paper defines the "Single Agent in a Team Decision" (SATD) problem. SATD differs from prior multi-agent communication problems in the assumptions it makes about teammates' knowledge of each other's plans and possible observations. The paper proposes a novel integrated logical-decision-theoretic approach to solving SATD problems, called MDP-PRT. Evaluation of MDP-PRT shows that it outperforms a previously proposed communication mechanism that did not consider the timing of communication and compares favorably with a coordinated Dec-POMDP solution that uses knowledge about all possible observations.

The Fisher market model is one of the most fundamental resource allocation models in economics. In a Fisher market, the prices and allocations of goods are determined according to the preferences and budgets of buyers to clear the market. In a Fisher market game, however, buyers are strategic and report their preferences over goods; the market-clearing prices and allocations are then determined based on their reported preferences rather than their real preferences. We show that the Fisher market game always has a pure Nash equilibrium, for buyers with linear, Leontief, and Cobb-Douglas utility functions, which are three representative classes of utility functions in the important Constant Elasticity of Substitution (CES) family. Furthermore, to quantify the social efficiency, we prove Price of Anarchy bounds for the game when the utility functions of buyers fall into these three classes respectively.

The health care literature argues compellingly that teamwork Figure 1: The care team for children with complex conditions. is of increasing importance to health care delivery, and improved care coordination is essential to improving patient safety and health. The lack of effective mechanisms to support health care providers in coordinating care is a major 1997), often base their communication mechanisms on theories deficiency of current health care systems (Leape 2012). My of teamwork and collaboration (Grosz and Kraus 1996; thesis aims to develop agents that support the coordination Cohen and Levesque 1990; Sonenberg et al. 1992). These of teams caring for children with complex conditions (Amir approaches, however, typically do not reason about uncertainty et al. 2013).

The internet has become a resource for adolescents who are distressed by social and emotional problems. Social network analysis can provide new opportunities for helping people seeking support online, but only if we understand the salient issues that are highly relevant to participants personal circumstances. In this paper, we present a stacked generalization modeling approach to analyze an online community supporting adolescents under duress. While traditional predictive supervised methods rely on robust hand-crafted feature space engineering, mixed initiative semi-supervised topic models are often better at extracting high-level themes that go beyond such feature spaces. We present a strategy that combines the strengths of both these types of models inspired by Prevention Science approaches which deals with the identification and amelioration of risk factors that predict to psychological, psychosocial, and psychiatric disorders within and across populations (in our case teenagers) rather than treat them post-facto. In this study, prevention scientists used a social science thematic analytic approach to code stories according to a fine-grained analysis of salient social, developmental or psychological themes they deemed relevant, and these are then analyzed by a society of models. We show that a stacked generalization of such an ensemble fares better than individual binary predictive models.

We study a simple sequential allocation mechanism for allocating indivisible goods between agents in which agents take turns to pick items.We focus on agents behaving strategically. We view the allocation procedure as a finite repeated game with perfect information. We show that with just two agents, we can compute the unique subgame perfect Nash equilibrium in linear time. With more agents, computing the subgame perfect Nash equilibria is more difficult. There can be an exponential number of equilibria and computing even one of them is PSPACE-hard. We identify a special case, when agents value many of the items identically, where we can efficiently compute the subgame perfect Nash equilibria. We also consider the effect of externalities and modifications to the mechanism that make it strategy proof.

Social choice theory provides insights into a variety of collective decision making settings, but nowadays some of its tenets are challenged by internet environments, which call for dynamic decision making under constantly changing preferences. In this paper we model the problem via Markov decision processes (MDP), where the states of the MDP coincide with preference profiles and a (deterministic, stationary) policy corresponds to a social choice function. We can therefore employ the axioms studied in the social choice literature as guidelines in the design of socially desirable policies. We present tractable algorithms that compute optimal policies under different prominent social choice constraints. Our machinery relies on techniques for exploiting symmetries and isomorphisms between MDPs.

For decades researchers have struggled with the problem of envy-free cake cutting: how to divide a divisible good between multiple agents so that each agent likes his own allocation best. Although an envy-free cake cutting protocol was ultimately devised, it is unbounded, in the sense that the number of operations can be arbitrarily large, depending on the preferences of the agents. We ask whether bounded protocols exist when the agents' preferences are restricted. Our main result is an envy-free cake cutting protocol for agents with piecewise linear valuations, which requires a number of operations that is polynomial in natural parameters of the given instance.

In 1950, when Turing proposed to replace the question "Can machines think?" computer science was not yet a field of study, Shannon's theory of information had just begun to change the way people thought about communication, and psychology was only starting to look beyond behaviorism. In the decades since that paper appeared, with its inspiring challenges, research in computer science, neuroscience, and the behavioral sciences has radically changed thinking about mental processes and communication, and the ways in which people use computers has evolved even more dramatically. This paper considers what that might be in light of Turing's paper and advances in the decades since it was written.

The AAAI-12 Workshop program was held Sunday and Monday, July 22–23, 2012 at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included 9 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, AI for Data Center Management and Cloud Computing, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, Semantic Cities. This article presents short summaries of those events.