Procedural content generation (PCG) for videogames relies on a commitment to the semantics of the game. Concepts such as enemies or solidity are required for the creation of levels for platformer games. As humans, we can instantly identify the underlying semantics of a game from brief snippets of game play video or from playing the game. Previous PCG systems have needed humans to identify the semantic properties of objects in the game, either implicitly or explicitly. We propose a system that can automatically learn the semantic properties of game objects by observation of events in the game via a causal learning framework. We apply this learning approach to play traces from the Super Mario Bros. series.

While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” often limited to what can be handencoded by system authors. Proceduralist readings, a way of deriving meaning for games based on their underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher-level meanings (e.g., deriving dynamics from mechanics), opening the door to broader and more culturally-situated game generation.

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.

For young children, adults learning English, or individuals with language disorders, complex narratives are difficult to create and understand.  While narratives can easily be assessed in terms of their lexical and syntactic difficulty, automatically measuring the level of narrative complexity is a challenging problem.  We present and evaluate a preliminary system for assessing narrative complexity, which should help identify suitable texts for readers and assist in narrative skill evaluation.

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.

Sentiment classification provides information about the author's feeling toward a topic through the use of expressive words. However, words indicative of a particular sentiment class can be domain-specific. We train a text classifier for Twitter data related to games using labels inferred from emoticons. Our classifier is able to differentiate between positive and negative sentiment tweets labeled by emoticons with 75.1% accuracy. Additionally, we test the classifier on human-labeled examples with the additional case of neutral or ambiguous sentiment. Finally, we have made the data available to the community for further use and analysis.

A wide variety of strategies have been used to create agents in the growing field of real-time strategy AI. However, a frequent problem is the necessity of hand-crafting competencies, which becomes prohibitively difficult in a large space with many corner cases. A preferable approach would be to learn these competencies from the wealth of expert play available. We present a system that uses the Generalized Sequential Pattern (GSP) algorithm from data mining to find common patterns in StarCraft:Brood War replays at both the micro- and macro-level, and verify that these correspond to human understandings of expert play. In the future, we hope to use these patterns to learn tasks and goals in an unsupervised manner for an HTN planner.