Collaboration between agents and players within games is a ripe area for exploration. As with adversarial AI, collaborative agents are challenged to accurately model players and adapt their behavior accordingly. The task of cooperation, however, allows for communication between teammates that can prove beneficial in coordinating joint actions and plans. Furthermore, we propose extending established multi-agent communication paradigms to include transfer of information pertinent to player models. By querying goal and preference information from a player, an agent can reduce uncertainty in coordination domains, allowing for more effective planning. We discuss the challenges as well as the planned development and evaluation of the system.

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.

This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also provides an analysis of the given games in terms of inherent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game playing technique.

Real-Time Strategy (RTS) games have become an increasingly popular test-bed for modern artificial intelligence techniques. With this rise in popularity has come the creation of several annual competitions, in which AI agents (bots) play the full game of StarCraft: Broodwar by Blizzard Entertainment. The three major annual StarCraft AI Competitions are the Student StarCraft AI Tournament (SSCAIT), the Computational Intelligence in Games (CIG) competition, and the Artificial Intelligence and Interactive Digital Entertainment (AIIDE) competition. In this paper we will give an overview of the current state of these competitions, and the bots that compete in them.

Video games presents an immense dissonance between its narrative and interactive sections. These two elements are commonly presented in a format where each occurs at a certain time, but rarely simultaneously. Besides, other elements, such as the background music, are also usually pre-defined, denying for the users the condition to establish new forms of creative expression within the system. This work, therefore, intends to propose new systems and algorithms to fit this paradigm.

My thesis aims at conceptualizing and implementing a computational model of narrative generation that is informed by narratological theory as well as cognitive multi-agent simulation models. It approaches this problem by taking a mimetic stance towards fictional characters and investigates how narrative phenomena related to characters can be computationally recreated from a deep character model grounded in multi agent systems. Based on such a conceptualization of narrative it explores how the generation of plot can be controlled, and how the quality of the resulting plot can be evaluated, in dependence of fictional characters. By that it contributes to research on computational creativity by implementing an evaluative storytelling system, and to narratology by proposing a generative narrative theory based on several post-structuralist descriptive theories.

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However, the performance of these various agents is non-transitive and varies significantly across different levels. No single agent dominates all situations presented, indicating that different procedures are better at solving certain levels than others. We therefore propose the construction of a hyper-agent that selects from a portfolio of sub-agents whichever it believes is best at solving any given level. This hyper-agent utilises key features that can be observed about a level to rank the available candidate algorithms based on their expected score.The proposed method exhibits a significant increase in performance over the individual sub-agents, and demonstrates the potential of using such an approach to solve other physics-based games or problems.

Physical site security heavily relies on expert teams continually examining and testing security profiles for discovering potential vulnerabilities. These experts hypothesize scenario(s) of interest and conduct "red versus blue" simulated exercises where they execute tactics that might reveal possible dangers. Due to the intensive manpower required, video-game environments have become a widely-adopted mechanism for conducting these exercises with virtual agents replacing many of the human roles for quicker analyses. However, these agents either have limited capabilities or require several engineers to develop realistic behaviors. This paper documents an agent architecture and authoring suite that enables subject matter experts to easily build complex attack/response plans for agents to use within Dante, a 3D simulation platform for video-game-based training/analysis of force-on-force engagements. This work expands upon current trends in commercial video-game artificial intelligence (AI) architectures to build agent behaviors deemed qualitatively valid by security experts, with the runtime of these algorithms best suited for turn-based, strategy games.

This paper makes a contribution to the advancement of artificial intelligence in the context of multi-agent planning for large-scale combat scenarios in RTS games. This paper introduces Fast Random Genetic Search (FRGS), a genetic algorithm which is characterized by a small active population, a crossover technique which produces only one child, dynamic mutation rates, elitism, and restrictions on revisiting solutions. This paper demonstrates the effectiveness of FRGS against a static AI and a dynamic AI using the Portfolio Greedy Search (PGS) algorithm. In the context of the popular Real-Time Strategy (RTS) game, StarCraft, this paper shows the advantages of FRGS in combat scenarios up to the maximum size of 200 vs. 200 units under a 40 ms time constraint.

Rational agents are becoming prevalent in many domains, from data analysis to entertainment and games. The increased prevalence of agents has evolved new tools and techniques to work with and design new agents. One such technique is system simulation. Systems simulation is a technique an author can use to imitate tasks, processes, or systems, and in particular, agents. Systems simulation has a variety of uses, ranging from simulating ecological systems to entertainment, such as interactive narratives and digital games. However, many system simulators use specialized programming languages and require prior programming experience.