The automatic orchestration problem is that of assigning instruments or sounds to the notes of an unorchestrated score. This is related to, but distinct from, problems of automatic expressive interpretation. A simple algorithm is described that successfully orchestrates scores based on analysis of one musical structure -- the "Z-chain."

The high degree of difficulty leads academic Despite increasing academic interest in video game Artificial researchers to create complex AI agents which attempt Intelligence (AI) over the past decade, and a rapidly to deal with the challenges but require large amounts changing games industry which often competes on new technology of development effort. The simpler commercial game agents (Laird and VanLent 2001), AI in video games has not are unable to adapt to situations unforeseen by their developers, improved as much as graphics, sound, animation or gameplay making them monotonous or easily exploitable (Mehta et al. 2009; Tozour 2002). Adoption of academic by human players (Baumgarten, Colton, and Morris 2009; research in AI is slow, likely because the industry Tozour 2002).

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.

Building complete interactive narrative systems is hard. Building systems that are satisfying for naïve users is especially hard since small deficiencies in component technologies can easily destroy the experience for a user. In this paper I argue that we can ameliorate some of these technical limitations through careful choice of genre and style, and discuss a number of properties of farce that make it a particularly attractive choice. Then I will describe work in progress on Punch and Judy AI Playset, a system that allows users to explore possible narratives in the Punch and Judy story world.

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.

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.

Authoring narrative content for interactive digital media can be both difficult and time consuming.The research proposed here aims at enhancing the capabilities of content creators through the development of a computational model that improves the quality of automatically generated stories, potentially decreasing the burden placed on the author. The quality and believability of a story can be significantly enhanced by the presence of compelling characters. To achieve this objective, I aim to develop a choice-based computational model that facilitates the automatic generation of narrative that includes characters that are made more compelling by the presence of distinct personality characteristics.

In recent years various games have been developed to generate useful data for scientific and commercial purposes. Current human computation games are tailored around a task they aim to solve, adding game mechanics to conceal monotonous workflows. These gamification approaches, although providing valuable gaming experience, do not cover the wide range of experiences seen in digital games today. This work presents a new use for design concepts for human computation games and an evaluation of player experiences.

Spatiotemporal reasoning is a fundamental contributor to effective problem solving. In an effort to design better problem-solving agents, we examined and evaluated the strategies that humans use to solve Tower Defense puzzles, a complex and popular class of real-time strategy games. A consistent and unexpected finding was that humans frequently treated time and space as equivalent. Players stated temporal goals but solved spatial problems. An analysis of human data and computer simulations showed that re-representing temporal problems as spatial problems was beneficial, but treating the two separately can lead to higher scores. The work presented here holds several possibilities for level designers and others who design and analyze maps and spatial arrangements for domains requiring strategic reasoning.

Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance was achieved through the use of case-based reasoning. In this framework, the agent optimally solves a set of problems and stores their solutions in a case base. Then, given any new problem, it seeks a similar case in the case base and uses its solution as an aid to solve the problem at hand. A number of ad hoc approaches to the case base formation problem have been proposed and empirically shown to perform well. In this paper, we investigate a theoretically driven approach to solving the problem. We mathematically relate properties of a case base to the suboptimality of the solutions it produces and subsequently develop an algorithm that addresses these properties directly. An empirical evaluation shows our new algorithm outperforms the existing state of the art on contemporary video-game pathfinding benchmarks.