10
Jul
Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: believable agents, case-based reasoning, games, goal-driven learning, meta-reasoning, real-time cbr, rts games. Leave a comment
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. The lack of adaptivity causes a break in player experience when they repeatedly fail to behave properly in circumstances unforeseen by the game designers.
We present an AI technique for game-playing agents that helps them adapt to changing game circumstances. The agents carry out runtime adaptation of their behavior sets by monitoring and reasoning about their behavior execution and using this reasoning to dynamically revise their behaviors. The evaluation of the behavior adaptation approach in a complex real-time strategy game shows that the agents adapt themselves and improve their performance by revising their behavior sets appropriately.
Read the paper:
Meta-Level Behavior Adaptation in Real-Time Strategy Games
by Manish Mehta, Santi Ontañon, Ashwin Ram
ICCBR-10 Workshop on Case-Based Reasoning for Computer Games, Alessandria, Italy, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-10-02.pdf
10
Jul
Posted by cognitivecomputing in Agents, Health & Wellness, Language, Web / Web 2.0. Tagged: case-based reasoning, healthcare, information retrieval, natural language, semantic memory, text cbr. Leave a comment
We introduce a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation to provide contextually relevant social and web search recommendations. Cobot supports the information discovery process by integrating web information retrieval along with proactive connections to relevant users who can participate in real-time conversations. We describe the conversational framework and report on some preliminary experiments in the system.
Read the paper:
Conversational Framework for Web Search and Recommendations
by Saurav Sahay, Ashwin Ram
ICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-10-01.pdf