Abstract
The field of Explainable Artificial Intelligence has gained popularity in recent years, due to the need for users to understand AI-made decisions, in order to increase their trust in the AI system. However, not much work has been performed on explaining recommendations made by search algorithms, which do not focus on single decisions, but on complex plans of action. This paper investigates promising directions for research in Explainable Search (XS), by evaluating with a user study different types of explanations for a search-based algorithm. Preliminary results suggest that users prefer explanations generated using context-based features, which are not only based on the current state of the problem, but are extracted from different parts of the tree generated by the search algorithm.
Original language | English |
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Title of host publication | 2023 IEEE Conference on Games (CoG) |
Publisher | IEEE |
Pages | 1-4 |
ISBN (Electronic) | 979-8-3503-2277-4 |
ISBN (Print) | 979-8-3503-2278-1 |
DOIs | |
Publication status | Published - 21 Aug 2023 |
Event | 2023 IEEE Conference on Games (CoG) - Boston, United States Duration: 21 Aug 2023 → 24 Aug 2023 https://2023.ieee-cog.org/ |
Conference
Conference | 2023 IEEE Conference on Games (CoG) |
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Abbreviated title | IEEE CoG |
Country/Territory | United States |
City | Boston |
Period | 21/08/23 → 24/08/23 |
Internet address |