Abstract
In this work, we introduce a technique that uses stories to train virtual agents to exhibit believable behavior. This technique uses a compact representation of a story to define the space of acceptable behaviors and then uses this space to assign rewards to certain world states. We show the effectiveness of our technique with a case study in a modified gridworld environment called Pharmacy World. The results show that a reinforcement learning agent using Q-learning was able to learn a policy that results in believable behavior.
| Original language | English |
|---|---|
| Title of host publication | WS-16-01 |
| Subtitle of host publication | Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence |
| Pages | 746-750 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781577357599 |
| State | Published - 2016 |
| Event | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: Feb 12 2016 → Feb 17 2016 |
Publication series
| Name | AAAI Workshop - Technical Report |
|---|---|
| Volume | WS-16-01 - WS-16-15 |
Conference
| Conference | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|---|
| Country/Territory | United States |
| City | Phoenix |
| Period | 2/12/16 → 2/17/16 |
Bibliographical note
Publisher Copyright:Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding
This material is based upon work supported by the U.S. Defense Advanced Research Projects Agency (DARPA) under Grant #D11AP00270 and the Office of Naval Research (ONR) under Grant #N00014-14-1-0003.
| Funders | Funder number |
|---|---|
| Office of Naval Research | 00014-14-1-0003 |
| Defense Advanced Research Projects Agency | 11AP00270 |
ASJC Scopus subject areas
- General Engineering