Computationally Viable Handling of Beliefs in Arguments for Persuasion
| Autor: | Anthony Hunter, Emmanuel Hadoux |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Value (ethics) Computational model Persuasion business.industry Computer science media_common.quotation_subject 030106 microbiology Probabilistic logic 02 engineering and technology 03 medical and health sciences Belief distribution Joint probability distribution Argument 0202 electrical engineering electronic engineering information engineering Independence (mathematical logic) 020201 artificial intelligence & image processing Artificial intelligence business Set (psychology) Random variable media_common |
| Zdroj: | ICTAI |
| DOI: | 10.1109/ictai.2016.0056 |
| Popis: | Computational models of argument are being developed to capture aspects of how persuasion is undertaken. Recent proposals suggest that in a persuasion dialogue between some agents, it is valuable for each agent to model how arguments are believed by the other agents. Beliefs in arguments can be captured by a joint belief distribution over the arguments and updated as the dialogue progresses. This information can be used by the agent to make more intelligent choices of move in the dialogue. Whilst these proposals indicate the value of modelling the beliefs of other agents, there is a question of the computational viability of using a belief distribution over all the arguments. We address this problem in this paper by presenting how probabilistic independence can be leveraged to split this joint distribution into an equivalent set of distributions of smaller size. Experiments show that updating the belief on the split distribution is more efficient than performing updates on the joint distribution. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|