Beyond the goal of refining system design to the needs and tastes of users, user evaluation of interactive music systems offers a method of examining the nature of musical creativity as understood by its human practitioners. In the case of improvising music systems, user study and evaluation of a system’s ability to improvise may be useful in the ethnomusicological study of musical interaction in contemporary improvised music. A survey of preliminary findings based on the interactions of an improvising system, Maxine, with several improvisers is discussed, with results suggesting methodological reconfigurations of the purpose and goals of evaluating of interactive musical metacreations.

Influence Maps have been successfully used in controlling the navigation of multiple units. In this paper, we apply the idea to the problem of simulating a kiting behavior (also known as ¨attack and flee'¨) in the context of real-time strategy (RTS) games. We present our approach and evaluate it in the popular RTS game StarCraft, where we analyze the benefits that our approach brings to a StarCraft playing bot.

This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games' state (not only player’s orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strate- gic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components.

We describe a heuristic search technique for multi-agent pursuit-evasion games in partially observable Euclidean space where a team of trackers attempt to minimize their uncertainty about an evasive target. Agents' movement and observation capabilities are restricted by polygonal obstacles, while each agent's knowledge of the other agents is limited to direct observation or periodic updates from team members. Our polynomial-time algorithm is able to generate strategies for games in continuous two-dimensional Euclidean space, an improvement over past algorithms that were only applicable to simple gridworld domains. We demonstrate that our algorithm is tolerant of interruptions in communication between agents, continuing to generate good strategies despite long periods of time where agents are unable to communicate directly. Experiments also show that our technique generates effective strategies quickly, with decision times of less than a second for reasonably sized domains with six or more agents.

Complex games such as RTS games are naturally formalized as Markov games. Given a Markov game, it is often possible to hand-code or learn a set of policies that capture the diversity of possible strategies. It is also often possible to hand-code or learn an abstract simulator of the game that can estimate the outcome of playing two strategies against one another from any state. We consider how to use such policy sets and simulators to make decisions in large Markov games. Prior work has considered the problem using an approach we call minimax policy switching. At each decision epoch, all policy pairs are simulated against each other from the current state, and the minimax policy is chosen and used to select actions until the next decision epoch. While intuitively appealing, we show that this switching policy can have arbitrarily poor worst case performance. In response, we describe a modified algorithm, monotone policy switching, whose worst case performance, under certain conditions, is provably no worse than the minimax fixed policy in the set. We evaluate these switching policies in both a simulated RTS game and the real game Wargus. The results show the effectiveness of policy switching when the simulator is accurate, and also highlight challenges in the face of inaccurate simulations.

We present CLASS Q-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASS Q-L uses a single table for each class  of unit so that each unit is controlled and updates its class’ Q-table. This enables rapid learning as in Wargus there are many units of the same class. We present initial results of CLASS Q-L against a variety of opponents.

Real-time strategy (RTS) games are known to be one of the most complex game genres for humans to play, as well as one of the most difficult games for computer AI agents to play well. To tackle the task of applying AI to RTS games, recent techniques have focused on a divide-and-conquer approach, splitting the game into strategic components, and developing separate systems to solve each. This trend gives rise to a new problem: how to tie these systems together into a functional real-time strategy game playing agent. In this paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.

This paper describes our ongoing effort to build an empathizing and adaptive storyteller system. The system under development aims to utilize emotional expressions generated from an avatar or a humanoid robot in addition to the listener’s responses which are monitored in real time, in order to deliver a story in an effective manner. We conducted a pilot study and the results were analyzed in two ways: first, through a survey questionnaire analysis based on the participant’s subjective ratings; second, through automated video analysis based on the participant’s emotional facial expression and eye blinking. The survey questionnaire results show that male participants have a tendency of more empathizing with a story character when a virtual storyteller is present, as compared to audio-only narration. The video analysis results show that the number of eye blinking of the participants is thought to be reciprocal to their attention.

The Intentional Fast-Forward (IFF) planner is an attempt to apply fast forward-chaining state-space search methods to intentional planning---planning such that every action is directed toward some character's goal. The IFF heuristic is based on Hoffmann's original Fast Forward heuristic (2001), which solves a simplified version of the problem and uses that solution as a guide for the real problem. IFF incorporates constraints imposed by intentional planning to narrow down the set of steps which can be taken next, and it identifies fruitless branches of the search space early.

In this paper, we discuss motivations for studying interactive narrative in shared, persistent worlds using the established conventions of quest-based MMORPGs.  We present a framework for categorizing the various techniques used in these games according to the interaction between the world model and the quest model .  Using this framework we generalize recent games to present a more dynamic world model, and investigate extensions to the quest model to support storytelling through adaptive quest narratives.