Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players’ intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models. The framework integrates multiple metrics for player goal recognition models, including two novel metrics, n-early convergence rate and standardized convergence point . We demonstrate the application of the GOALIE framework with the evaluation of several player goal recognition models, including Markov logic network-based, deep feedforward neural network-based, and long short-term memory network-based goal recognizers on two different educational games. The results suggest that GOALIE effectively captures goal recognition behaviors that are key to next-generation player modeling.

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

Exploring web and in particular social media data is an essential task to many of the subject matter experts in order to discover content around their subject of interest. It is important to provide them with a tool to define their scope of vocabulary, i.e what to search for, and suggest them commonly used terms besides the serendipitous terms allowing them to define their scope of explorations. This paper presents methods on constructing ``domain models" which are families of keywords and extractors to enable focus on social media documents relevant to a project using multiple channels of information extraction.

An accurate problem list plays the key role of a problem-oriented medical record, which plays a significant role in improving patient care. However, the multi-author, multi-purpose nature of problem list makes it a challenge to maintain, and a single list is difficult, if not impossible, to satisfy all the needs of different practitioners. In this paper, we propose using machine generated problem list to assist a medical practitioner to review a patient’s chart. The proposed system scans both structured and unstructured data in a patient’s electronic medical record (EMR) and generates a ranked, recall-oriented problem list grouped by body systems. Details of each problem are readily available for the user to assess the correctness and relevance of the problem. The user can then provide feedback to the system on the trustworthiness of each evidence passage retrieved, as well as the validity of the problem as a whole. The user-specific feedback provides new information the system needs to perform active learning to learn the user’s preference and produce personalized, and/or domain-specific problem lists.

David L. Waltz (1943-2012), was director, Center for Computational Learning Systems In 1973, Dave Waltz with Richard P. Gabriel in tow headed Dave Waltz delivers his AAAI Presidential Address at AAAI-98 in Madison, Wisconsin. While at Illinois, Dave produced system, paving the way for an engineering-style 11 Ph.D. students and many more MS students, approach to emergent AI techniques; and even mentored junior researchers and postdocs, attracted though their first attempts to create a multidisciplinary new AI faculty, and helped create the Beckman AI degree program failed, Dave was able in Institute for Advanced Science and Technology. In 1984, Marvin Minsky asked Dave to return to During the late 1970s and early 1980s, Waltz's Thinking Machines, Inc., an MIT spinoff in Cambridge group explored new ideas in natural language processing, -- with the temptation that the atmosphere cognitive science, qualitative reasoning, would be like the early days of the AI Lab all over and parallel computation in a collaborative environment again. At the same time he took a parttime including researchers in computer science, tenured position at Brandeis. Machines and Brandeis, Dave developed the ideas He chaired and brought the influential of massively parallel AI and, with Craig Stanfill, the Theoretical Issues in Natural Language Processing memory-based reasoning approach to case-based conference to Urbana in 1978.

Of course, this is far from the first attempt to plot a course toward human-level AGI: arguably this was the goal of the founders of the field of artificial intelligence in the 1950s, and has been pursued by a steady stream of AI researchers since, even as the majority of the AI field has focused its attention on more narrow, specific subgoals. The ideas presented here build on the ideas of others in innumerable ways, but to review the history of AI and situate the current effort in the context of its predecessors would require a much longer article than this one. Thus we have chosen to focus on the results of our AGI roadmap discussions, acknowledging in a broad way the many debts owed to many prior researchers. References to the prior literature on evaluation of advanced AI systems are given by Laird (Laird et al. 2009) and Geortzel and Bugaj (2009), which may in a limited sense be considered prequels to this article. We begin by discussing AGI in general and adopt a pragmatic goal for measuring progress toward its attainment. An initial capability landscape for AGI The heterogeneity of general intelligence in will be presented, drawing on major themes from humans makes it practically impossible to develop developmental psychology and illuminated by a comprehensive, fine-grained measurement system mathematical, physiological, and informationprocessing for AGI. While we encourage research in defining perspectives. The challenge of identifying such high-fidelity metrics for specific capabilities, appropriate tasks and environments for measuring we feel that at this stage of AGI development AGI will be taken up. Several scenarios will a pragmatic, high-level goal is the best we can be presented as milestones outlining a roadmap agree upon. I advocate beginning with a system that has minimal, although extensive, built-in capabilities. Many variant approaches have been proposed A classic example of the narrow AI approach was for achieving such a goal, and both the AI and AGI IBM's Deep Blue system (Campbell, Hoane, and communities have been working for decades on Hsu 2002), which successfully defeated world chess the myriad subgoals that would have to be champion Gary Kasparov but could not readily achieved and integrated to deliver a comprehensive apply that skill to any other problem domain without AGI system.

Narratives are ubiquitous in human experience. We use them - What comprises the set of possible narrative arcs? Is there to educate, communicate, convince, explain, and entertain. How many possible story lines are there? Is As far as we know, every society in the world has narratives, there a recipe (à la Joseph Campbell or Vladimir Propp) which suggests they are rooted in our psychology and serve for generating narratives? an important cognitive function: that narratives do something - What are the appropriate representations of narrative?

We investigate the impact of a discussion snippet's overall sentiment on a user's willingness to read more of a discussion. Using sentiment analysis, we constructed positive, neutral, and negative discussion snippets using the discussion topic and a sample comment from discussions taking place around content on an enterprise social networking site. We computed personalized snippet recommendations for a subset of users and conducted a survey to test how these recommendations were perceived. Our experimental results show that snippets with high sentiments are better discussion "teasers."