Abstract
In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl. The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables - whose values are determined by factors beyond our model - in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial. Inertial unknown variables are helpful in assimilating observations and modeling counterfactuals and causality; while non-inertial unknown variables help characterize stochastic behavior, such as the outcome of tossing a coin, that are not impacted by observations. Finally, we give a glimpse of incorporating probabilities into reasoning with narratives.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
| Pages | 507-512 |
| Number of pages | 6 |
| State | Published - 2002 |
| Event | 18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02) - Edmonton, Alta., Canada Duration: Jul 28 2002 → Aug 1 2002 |
Other
| Other | 18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02) |
|---|---|
| Country | Canada |
| City | Edmonton, Alta. |
| Period | 7/28/02 → 8/1/02 |
ASJC Scopus subject areas
- Software
Cite this
Reasoning about actions in a probabilistic setting. / Baral, Chitta; Tran, Nam; Tuan, Le Chi.
Proceedings of the National Conference on Artificial Intelligence. 2002. p. 507-512.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Reasoning about actions in a probabilistic setting
AU - Baral, Chitta
AU - Tran, Nam
AU - Tuan, Le Chi
PY - 2002
Y1 - 2002
N2 - In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl. The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables - whose values are determined by factors beyond our model - in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial. Inertial unknown variables are helpful in assimilating observations and modeling counterfactuals and causality; while non-inertial unknown variables help characterize stochastic behavior, such as the outcome of tossing a coin, that are not impacted by observations. Finally, we give a glimpse of incorporating probabilities into reasoning with narratives.
AB - In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl. The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables - whose values are determined by factors beyond our model - in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial. Inertial unknown variables are helpful in assimilating observations and modeling counterfactuals and causality; while non-inertial unknown variables help characterize stochastic behavior, such as the outcome of tossing a coin, that are not impacted by observations. Finally, we give a glimpse of incorporating probabilities into reasoning with narratives.
UR - http://www.scopus.com/inward/record.url?scp=0036923234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036923234&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0036923234
SP - 507
EP - 512
BT - Proceedings of the National Conference on Artificial Intelligence
ER -