Game scholars need to find moments in games that advance their arguments, and artificial intelligence algorithms need to recall states that are most promising for exploration. This paper considers the problem of engineering a representation for game states that is suitable for retrieval in the vector space model. Retrieving moments from gameplay traces for two popular Super Nintendo Entertainment System games, we evaluate several different representations including one derived from a deep embedding of screenshot pixels based on a supervised memory prediction task. The results suggest compact moment vectors may be a promising representation for building future systems that intend to build higher level knowledge about games.

Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps and objects, and inability to use that data to learn more about how humans perform and evaluate grasps. This paper presents a data capture protocol that partially addresses these deficiencies by asking participants to specify ranges over which a grasp is valid. The protocol is verified both qualitatively through online survey questions (where within-range grasps are identified correctly with the nearest extreme grasp) and quantitatively by showing that there is small variation in grasps ranges from different participants as measured by joint angles and position. We demonstrate that these grasp ranges are valid through testing on a physical robot (93.75% of grasps interpolated from grasp ranges are successful).

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.

Within multiagent teams research, existing approaches commonly assume agents have perfect knowledge regarding the decision process guiding their teammates' actions. More recently, ad hoc teamwork was introduced to address situations where an agent must coordinate with a variety of potential teammates, including teammates with unknown behavior. This paper examines the communication of intentions for enhanced coordination between such agents. The proposed decision-theoretic approach examines the uncertainty within a model of an unfamiliar teammate, identifying policy information valuable to the collaborative effort. We characterize this capability through theoretical analysis of the computational requirements as well as empirical evaluation of a communicative agent coordinating with an unknown teammate in a variation of the multiagent pursuit domain.

Dunyazad is a system which creates narrative choices à la Choose-Your-Own-Adventure books. It attempts to generate choices that achieve specific poetic effects. This paper demonstrates Dunyazad’s ability to manage player expectations by having it generate three distinct choice structures: obvious choices, relaxed choices, and dilemmas. Using answer set programming, Dunyazad’s choice generation system directly encodes a theory of choice poetics, so flaws in its output can inform both the system and the theory itself. Survey data presented here thus not only validate that players’ perceptions match Dunyazad’s intentions, but also have implications for the theory of choice poetics. Statistical analysis of our data indicates that Dunyazad can successfully construct obvious choices, relaxed choices, and dilemmas.

Coordination with an unknown human teammate is a notable challenge for cooperative agents. Behavior of human players in games with cooperating AI agents is often sub-optimal and inconsistent leading to choreographed and limited cooperative scenarios in games. This paper considers the difficulty of cooperating with a teammate whose goal and corresponding behavior change periodically. Previous work uses Bayesian models for updating beliefs about cooperating agents based on observations. We describe belief models for on-line planning, discuss tuning in the presence of noisy observations, and demonstrate empirically its effectiveness in coordinating with inconsistent agents in a simple domain. Further work in this area promises to lead to techniques for more interesting cooperative AI in games.

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities -- Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

Maintaining an accurate set of beliefs in a partially observable scenario, particularly with respect to other agents operating in the same space, is a vital aspect of multiagent planning. We analyze how the beliefs of an agent can be updated for fast adaptivity to changes in the behavior of an unknown teammate. The main contribution of this paper is the empirical evaluation of an agent cooperating with a teammate whose goals change periodically. We test our approach in a collaborative multiagent domain where identification of goals is necessary for successful completion. The belief revision technique we propose outperforms the traditional approach in a majority of test cases. Additionally, our results suggest the ability to approximate a higher level model by utilizing a belief distribution over a set of lower level behaviors, particularly when the belief update strategy identifies changes in the behavior in a responsive manner.

We discuss a design pattern found in expressive storygames, the eurekon, which describes a specific dynamic arising from some adventure game puzzles where the player experiences a moment of revelation connecting the narrative and ludic planes. Eurekons have largely been designed out of modern storygames in favor of patterns that reduce the possibility of failure (as seen in the fall of the "puzzle" and rise of the "quest"), but this shift often eliminates the unique pleasures often found in a successful eurekon. We demonstrate both how the eurekon is a useful concept in analyzing existing adventure games and how it can inform designers hoping to create more successful eurekons.