We encounter the results of category formation every day, from demographic categories like race and gender, to role-playing-game classes like "fighter" or "mage". Category membership is often not simply based on the possession of discrete properties but instead constructed from and reflect the highly nuanced relationships (gradience) between members and best-example individuals called "prototypes". In this paper, we present The Marginal, an artificial intelligence (AI)-driven game that (1) computationally models the cognitive categories that players develop when customizing videogame avatars and (2) generates challenges for players to use their perception of visual, textual, and numerical data to progress in a game created using these models. We use archetypal analysis, an AI clustering approach for identifying boundary points in data, to generate tasks in The Marginal for its gameplay. It shows how AI can be combined with games to model and evaluate cognitive  categorization phenomena.

Videogame avatars provide an expressive avenue for players to represent themselves virtually. Research has shown that these avatars, while virtual, can reveal aspects of players' identities, along with physical, social, and cultural values of the real-world. In this paper, we present an approach for modeling player values through their avatars using artificial intelligence (AI) clustering techniques. In a study with 191 participants who created avatars using our system, we provide a thorough comparison of the techniques across numerical, textual, and visual data. Our findings showed that these data structures can effectively reveal players' values and preferences, such as conforming to stereotypes of character roles using statistical attributes, modeling nuances in text descriptions of avatars, and identifying "best-example" (prototypical) avatar appearances that players can be quantitatively shown to conform to. Our findings suggest that AI clustering approaches can be used to model players to yield insight into implicitly held values in a data-driven manner through virtual avatars.

Advanced robotic technology is opening up the possibility of integrating robots into the human workspace to improve productivity and decrease the strain of repetitive, arduous physical tasks currently performed by human workers. However, coordinating these teams is a challenging problem. We must understand how decision-making authority over scheduling decisions should be shared between team members and how the preferences of the team members should be included. We report the results of a human-subject experiment investigating how a robotic teammate should best incorporate the preferences of human teammates into the team's schedule. We find that humans would rather work with a robotic teammate that accounts for their preferences, but this desire might be mitigated if their preferences come at the expense of team efficiency.

The Markov Decision Process (MDP) framework is a versatile method for addressing single and multiagent sequential decision making problems. Many exact and approximate solution methods attempt to exploit structure in the problem and are based on value factorization. Especially multiagent settings (MAS), however, are known to suffer from an exponential increase in value component sizes as interactions become denser, meaning that approximation architectures are overly restricted in the problem sizes and types they can handle. We present an approach to mitigate this limitation for certain types of MASs, exploiting a property that can be thought of as "anonymous influence" in the factored MDP. In particular, we show how anonymity can lead to representational and computational efficiencies, both for general variable elimination in a factor graph but also for the approximate linear programming solution to factored MDPs. The latter allows to scale linear programming to factored MDPs that were previously unsolvable. Our results are shown for a disease control domain over a graph with 50 nodes that are each connected with up to 15 neighbors.

Recent research has demonstrated that emotion plays a key role in human decision making. Across a wide range of disciplines, old concepts, such as the classical ``rational actor" model, have fallen out of favor in place of more nuanced models (e.g., the frameworks of behavioral economics and emotional intelligence) that acknowledge the role of emotions in analyzing human actions. We now know that context, framing, and emotional and physiological state can all drastically influence decision making in humans. Emotions serve an essential, though often overlooked, role in our lives, thoughts, and decisions. However, it is not clear how and to what extent emotions should impact the design of artificial agents, such as social robots. In this paper I argue that enabling robots, especially those intended to interact with humans, to sense and model emotions will improve their performance across a wide variety of human-interaction applications. I outline two broad research topics (affective inference and learning from affect) towards which progress can be made to enable ``affect-aware" robots and give a few examples of applications in which robots with these capabilities may outperform their non-affective counterparts. By identifying these important problems, both necessary for fully affect-aware social robots, I hope to clarify terminology, assess the current research landscape, and provide goalposts for future research.

This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Some of its key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent systems; provides a large number of models for decision-theoretic decision making, including one-shot decision making (e.g., Bayesian games) and sequential decision making under various assumptions of observability and cooperation, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an extensive range of planning and learning algorithms for single-and multiagent systems; and is written in C++ and designed to be extensible via the object-oriented paradigm.

Multi-robot systems are an exciting application domain for AI research and Dec-POMDPs, specifically. MacDec-POMDP methods can produce high-quality general solutions for realistic heterogeneous multi-robot coordination problems by automatically generating control and communication policies, given a model. In contrast to most existing multi-robot methods that are specialized to a particular problem class, our approach can synthesize policies that exploit any opportunities for coordination that are present in the problem, while balancing uncertainty, sensor information, and information about other agents.

The Twenty-Ninth AAAI Conference on Artificial Intelligence, (AAAI-15) was held in January 2015 in Austin, Texas (USA) The conference program was cochaired by Sven Koenig and Blai Bonet. This report contains reflective summaries of the main conference, the robotics program, the AI and robotics workshop, the virtual agent exhibition, the what's hot track, the competition panel, the senior member track, student and outreach activities, the student abstract and poster program, the doctoral consortium, the women's mentoring event, and the demonstrations program.

The AAAI 2015 Spring Symposium Series was held Monday through Wednesday, March 23-25, at Stanford University near Palo Alto, California. The titles of the seven symposia were Ambient Intelligence for Health and Cognitive Enhancement, Applied Computational Game Theory, Foundations of Autonomy and Its (Cyber) Threats: From Individuals to Interdependence, Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Logical Formalizations of Commonsense Reasoning, Socio-Technical Behavior Mining: From Data to Decisions, Structured Data for Humanitarian Technologies: Perfect Fit or Overkill?

The digital clock drawing test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.