Agents that remember can tell stories

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI : 10.1109/DEVLRN.2007.4354039This paper describes a hierarchical design for knowledge representation in computational autobiographic memory. This model is named AMIA (Autobiographic Memory for Intelligent Agents). Inspired by research in human autobiographic memory from the areas of Psychology and Cognitive Science, the self memory system (SMS) is modelled as 1) the general knowledge structure of agents??? significant events and 2) the unique ???self??? that SMS creates for an individual agent. We first carry out a concentrated review of autobiographic memory, emotion and event, and how they relate to each other. For AMIA, we start with an overview for different levels of the overall organisation and interactions between levels. We then focus on the design at the bottom level of AMIA ??? Action and Event levels. We also discuss the role of general event representations (GERs) which create knowledge representations for routine and highly expected events to be understood by autonomous agents, in conjunction with support from the autobiographic knowledge base in the reconstruction process of autobiographic events. Finally, we discuss the possible agent implementations for future research directions

show abstract

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Dautenhahn

Lim

et al.

Virtual Learning Environments

View full text Add to dashboard Cite

This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modelling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottom-up approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.

show abstract

Agents that remember can tell stories

Cited by 4 publications

References 4 publications

Using a Parameterized Memory Model to Modulate NPC AI

Using a Parameterized Memory Model to Modulate NPC AI

AMIA: A knowledge representation model for computational autobiographic agents

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Contact Info

Product

Resources

About