Tags » Real-time Cbr

Case-Based Reasoning and User-Generated AI for Real-Time Strategy Games

Creating AI for complex computer games requires a great deal of technical knowledge as well as engineering effort on the part of game developers. This paper focuses on techniques that enable end-users to create AI for games without requiring technical knowledge by using case-based reasoning techniques. 88 more words


MMPM: A Generic Platform for Case-Based Planning Research

Computer games are excellent domains for research and evaluation of AI and CBR techniques. The main drawback is the effort needed to connect AI systems to existing games. 77 more words

Case-based Reasoning

Real-Time Case-Based Reasoning for Interactive Digital Entertainment

(Click image to view the video – it’s┬ánear the bottom of the new page.)

User-generated content is everywhere: photos, videos, news, blogs, art, music, and every other type of digital media on the Social Web. 426 more words


Meta-Level Behavior Adaptation in Real-Time Strategy Games

AI agents designed for real-time settings need to adapt themselves to changing circumstances to improve their performance and remedy their faults. Agents typically designed for computer games, however, lack this ability. 134 more words


Run-Time Behavior Adaptation for Real-Time Interactive Games

Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. 201 more words


Using Meta-Reasoning to Improve the Performance of Case-Based Planning

Case-based planning (CBP) systems are based on the idea of reusing past successful plans for solving new problems. Previous research has shown the ability of meta-reasoning approaches to improve the performance of CBP systems. 141 more words


Learning from Human Demonstrations for Real-Time Case-Based Planning

One of the main bottlenecks in deploying case-based planning systems is authoring the case-base of plans. In this paper we present a collection of algorithms that can be used to automatically learn plans from human demonstrations. 117 more words