Creating adaptive and individual personalities in many characters without hand crafting behaviours

Believable characters significantly increase the immersion of users or players in interactive applications. A key component of believable characters is their personality, which has previously been implemented statically using the time consuming task of hand-crafting individuality for each character. Often personality has been modeled based on theories that assume behavior is the same regardless of situation and environment. This paper presents a simple affective and cognitive framework for interactive entertainment characters that allows adaptation of behavior based on the environment and emotions. Different personalities are reflected in behavior preferences which are generated based on individual experience. An initial version of the framework has been implemented in a simple scenario to explore which parameters have the greatest effect on agent diversity

An improved approach to reinforcement learning in computer go

Monte-Carlo Tree Search (MCTS) has revolutionized, Computer Go, with programs based on the algorithm, achieving a level of play that previously seemed decades away., However, since the technique involves constructing a search tree, its performance t

Flexible story generation with Norms and Preferences in computer role playing games

Interactive storytelling is a strength of table-top role playing games as they are facilitated by a game master (GM) who directs the narrative and devises game scenarios. One difficulty with the implementation is the large amount of time

Bushfire blocks: A modular agent-based simulation

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Reusing skills for first-time solution of navigation tasks in platform videogames

We consider the problem of performing real-time navigation in domains where a "god's eye view"is provided. One setting where this challenge arises is in platform videogames, occurring whenever the player wishes to reach an item or powerup on the current screen. Previous agents for these games rely on generating many low-level simulations or training runs for each fixed task. Human players, on the other hand, can solve navigation tasks at a high level by visualising sequences of abstract "skills". Based on this intuition, we introduce a novel planning approach and apply it to Infinite Mario. Despite facing randomly generated, maze-like tasks, our agent is capable of deriving complex plans in real-time, without exploiting precise knowledge of the game's code

A BDI game master agent for computer role-playing games

In this paper we describe an approach for developing an intelligent game master (GM) for computer role-playing games. The role of the GM is to set up the game environment, manage the narrative flow and enforce the game rules whilst keeping the players engaged. Our approach is to use the popular Belief-Desire-Intention (BDI) model of agents to developing a GM

Implementing story-driven games with the aid of dynamical policy models

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Simulating sensory perception in 3D game characters

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Modelling interactive non-linear stories

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