Archive for July 21st, 2009

21 Jul

Collaborative Information Access: A Conversational Search Approach

Posted by cognitivecomputing in Agents, Health & Wellness, Language, Web / Web 2.0. Tagged: , , , .

Knowledge and user-generated content is proliferating on the web in scientific publications, information portals and online social media. This knowledge explosion has continued to outpace technological innovation in efficient information access technologies. In this paper, we describe methods and technologies for “Conversational Search” as an innovative solution to facilitate easier information access and reduce the information overload for users.

Conversational Search is an interactive and collaborative information finding interaction. The participants in this interaction engage in social conversations aided with an intelligent information agent (Cobot) that provides contextually relevant search recommendations. The collaborative and conversational search activity helps users make faster and more informed search and discovery. It also helps the agent learn about conversations with interactions and social feedback to make better recommendations. Conversational search leverages the social discovery process by integrating web information retrieval along with the social interactions.

Read the paper:

Collaborative Information Access: A Conversational Search Approach

by Saurav Sahay, Anu Venkatesh, Ashwin Ram

ICCBR-09 Workshop on Reasoning from Experiences on the Web (WebCBR-09), Seattle, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-05.pdf

21 Jul

Authoring Behaviors for Games using Learning from Demonstration

Posted by cognitivecomputing in Game AI, Learning. Tagged: , , , .

Behavior authoring for computer games involves writing behaviors in a programming language. This method is cumbersome and requires a lot of programming effort to author the behavior sets. Further, this approach restricts the behavior set authoring to people who are experts in programming.

This paper describes our approach to design a system that allows a user to demonstrate behaviors to the system, which the system uses to learn behavior sets for a game domain. With learning from demonstration, we aim at removing the requirement that the user has to be an expert in programming, and only require him to be an expert in the game. The approach has been integrated in a easy-to-use visual interface and instantiated for two domains, a real-time strategy game and an interactive drama.

Read the paper:

Authoring Behaviors for Games using Learning from Demonstration

by Manish Mehta, Santiago Ontañón, Tom Amundsen, Ashwin Ram

ICCBR-09 Workshop on Case-Based Reasoning for Computer Games, Seattle, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-07.pdf