Standard game tree search algorithms, such as minimax or Monte Carlo Tree Search, assume the existence of an accurate forward model that simulates the effects of actions in the game. Creating such model, however, is a challenge in itself.One cause of the complexity of the task is the gap in level of abstraction between the informal specification of the model and its implementation language. To overcome this issue, we propose a technique for the implementation of forward models that relies on the Answer Set Programming paradigm and on well-established knowledge representation techniques from defeasible reasoning and reasoning about actions and change. We evaluate our approach in the context of Real-Time Strategy games using a collection of StarCraft scenarios.

This paper proposes an online transfer framework to capture the interaction among agents and shows that current transfer learning in reinforcement learning is a special case of online transfer. Furthermore, this paper re-characterizes existing agents-teaching-agents methods as online transfer and analyze one such teaching method in three ways. First, the convergence of Q-learning and Sarsa with tabular representation with a finite budget is proven. Second, the convergence of Q-learning and Sarsa with linear function approximation is established. Third, the we show the asymptotic performance cannot be hurt through teaching. Additionally, all theoretical results are empirically validated.

There has been much work on player modeling using game behavioral data collected. Many of the previous research projects that targeted this goal used aggregate game statistics as features to develop behavior models using both statistical and machine learning techniques. While existing methods have already led to interesting findings, we suspect that aggregated features discard valuable information such as temporal or sequential patterns, which may be important in deciphering information about decisionmaking, problem solving, or individual differences. Such sequential information is critical to analyze player behaviors especially in role-playing games (RPG) where players can face ample choices, experience different contexts, behave freely with individual propensities but possibly end up with similar aggregated statistics (e.g., levels, time spent). In this paper we intend to develop and apply a modeling technique that takes into consideration sequential patters to decipher individual differences in playing a Role Playing Game (RPG) game. Using an RPG with multiple affordances, we designed an experiment collecting granular in-game behaviors of 64 players. Using closed sequential pattern mining and logistic regression, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors. The contribution of this paper is the algorithms we developed combined with a validation procedure to determine the reliability and validity of the results and the results themselves.

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.

This paper explores the concept of Continuation Desire further by investigating the behavioral intent of players’ desire to keep playing. User experience is a complex, multifaceted topic, which is commonly studied through different aspects namely engagement, continuation desire, immersion, flow experience, motivation and enjoyment — yet it is difficult to measure. These concepts were conceptualized into different factors and thereby it was identified which of them are related. This resulted in a synthesized model that was based on the Theory of Planned Behavior model. This model takes into account the perceived user experience factors relevant for Continuation Desire and then attempts to predict players’ intention to continue playing. Structural Equation Modeling analysis was performed to validate the model and to predict the intention of continuation desire. At the same time, exploring why people continue playing, based on experiments using Candy Crush Saga, one of the most popular Free-to-Play mobile games worldwide. The findings indicate that motivation is an important factor of Continuation Desire in Free-to-Play mobile games, with engagement, enjoyment and flow being less important. This paper contributes an early work of a factor-based exploration of measuring user experience and their continuation desire.

We describe an experiment in using IBM’s Watson cognitive system to teach about human-computer co-creativity in a Georgia Tech Spring 2015 class on computational creativity. The project-based class used Watson to support biologically inspired design, a design paradigm that uses biological systems as analogues for inventing technological systems. The twenty-four students in the class self-organized into six teams of four students each, and developed semester-long projects that built on Watson to support biologically inspired design. In this paper, we describe this experiment in using Watson to teach about human-computer co-creativity, present one project in detail, and summarize the remaining five projects. We also draw lessons on building on Watson for (i) supporting biologically inspired design, and (ii) enhancing human-computer co-creativity.

We present a system for autonomously generating puzzles in the form of a 2D, tile-based world.  Puzzle design is entirely dependent on tile characteristics, which are implemented as abstract classes that can be modified by the system.  Thus, the system controls not only the base-level puzzle design but also (to some extent) the meta-level component design.  The result is a rich space of possible puzzles that the system explores with a combination of evolutionary computation and Q -learning.  The system autonomously produces a variety of puzzles of varying difficulty to create a game called Loki's Castle .  The system is almost completely autonomous, requiring only a minimal description of what a puzzle should include, and the abstraction allows extensibility so that future versions can invent entirely new classes of tiles.  Several puzzle examples are presented to demonstrate the system's capability.

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.

An ongoing goal at the intersection of artificial intelligence In this paper, we briefly present initial work that has (AI), robotics, and human-robot interaction (HRI) is to create been done in the DIARC/ADE cognitive robotic architecture autonomous agents that can assist and interact with human (Schermerhorn et al. 2006; Kramer and Scheutz 2006) to enable teammates in natural and humanlike ways. This is a such a rejection and explanation mechanism. First we multifaceted challenge, involving both the development of discuss the theoretical considerations behind this challenge, an ever-expanding set of capabilities (both physical and algorithmic) specifically the conditions that must be met for a directive to such that robotic agents can autonomously engage be appropriately accepted. Next, we briefly present some of in a variety of useful tasks, as well as the development the explicit reasoning mechanisms developed in order to facilitate of interaction mechanisms (e.g.

We introduce Playspecs, an application of omega-regular expressions to specifying play traces (sequences of game states or events unfolding over time). This connects the automated analysis and model checking of games to the literature on formal software verification via Bu ̈chi automata. We show how to define desirable or undesirable sequences of game events with Playspecs and how associated algorithms can find examples (or prove the impossibility) of such sequences. Playspecs have two main benefits over existing techniques for specifying the behaviors of a game over time. First, they offer a scalable commitment to formal modeling: the same Playspecs can filter existing traces gathered by telemetry, search for satisfying traces using existing game code, or drive formal verification when paired with a logical model of a game. Second, Playspecs' syntax can be customized for the game engine or game in question so designers may write specifications using their game's native vocabulary. We define Playspecs' syntax and semantics (modulo gamespecific customizations) and outline algorithms for each of the applications mentioned above, providing examples from the social simulation game Prom Week and the puzzle game engine PuzzleScript.