Results 1 -
3 of
3
An MDP Approach for Explanation Generation
- In Proceedings of the ExaCt 2007 Workshop on Explanation-aware Computing at AAAI 2007
, 2007
"... In order to assist a power plant operator to face un-usual situations, we have developed an intelligent as-sistant that explains the suggested commands generated by an MDP-based planning system. This assistant pro-vides the trainee a better understanding of the recom-mended actions to later generali ..."
Abstract
-
Cited by 5 (1 self)
- Add to MetaCart
(Show Context)
In order to assist a power plant operator to face un-usual situations, we have developed an intelligent as-sistant that explains the suggested commands generated by an MDP-based planning system. This assistant pro-vides the trainee a better understanding of the recom-mended actions to later generalize them to similar situ-ations. In a first stage, built-in explanations are prede-fined by a domain expert and encapsulated within ex-planation units. When the operator takes an incorrect action, an explanation is automatically generated. A controlled user study in this stage showed that expla-nations have a positive impact on learning. In a second stage, we are developing an automatic explanation gen-eration mechanism based on a factored representation of the decision model used by the planning system. As part of this stage, we describe an algorithm to select a relevant variable, which is a key component of the ex-planations defined by the expert.
An MDP Approach for Explanation Generation Francisco Elizalde1,3 1
"... In order to assist a power plant operator to face unusual situations, we have developed an intelligent assistant that explains the suggested commands generated by an MDP-based planning system. This assistant provides the trainee a better understanding of the recommended actions to later generalize t ..."
Abstract
- Add to MetaCart
(Show Context)
In order to assist a power plant operator to face unusual situations, we have developed an intelligent assistant that explains the suggested commands generated by an MDP-based planning system. This assistant provides the trainee a better understanding of the recommended actions to later generalize them to similar situations. In a first stage, built-in explanations are predefined by a domain expert and encapsulated within explanation units. When the operator takes an incorrect action, an explanation is automatically generated. A controlled user study in this stage showed that explanations have a positive impact on learning. In a second stage, we are developing an automatic explanation generation mechanism based on a factored representation of the decision model used by the planning system. As part of this stage, we describe an algorithm to select a relevant variable, which is a key component of the explanations defined by the expert.
The Why Agent Enhancing user trust in automation through explanation dialog
"... Abstract — Lack of trust in autonomy is a recurrent issue that is becoming more and more acute as manpower reduction pressures increase. We address the socio-technical form of this trust problem through a novel decision explanation approach. Our approach employs a semantic representation to capture ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract — Lack of trust in autonomy is a recurrent issue that is becoming more and more acute as manpower reduction pressures increase. We address the socio-technical form of this trust problem through a novel decision explanation approach. Our approach employs a semantic representation to capture decision-relevant concepts as well as other mission-relevant knowledge along with a reasoning approach that allows users to pose queries and get system responses that expose decision rationale to users. This representation enables a natural, dialogbased approach to decision explanation. It is our hypothesis that the transparency achieved through this dialog process will increase user trust in autonomous decisions. We tested our hypothesis in an experimental scenario set in the maritime autonomy domain. Participant responses on psychometric trust constructs were found to be significantly higher in the experimental group for the majority of constructs, supporting our hypothesis. Our results suggest the efficacy of incorporating a decision explanation facility in systems for which a sociotechnical trust problem exists or might be expected to develop.
