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Preference elicitation with interdependency and user bother cost

Preference elicitation with interdependency and user bother cost
Preference elicitation with interdependency and user bother cost

Agent-based scheduling systems, such as automated systems that schedule meetings for users and systems that schedule smart devices in smart homes, require the elicitation of user preferences in oder to operate in a manner that is consistent with user expectations. Unfortunately, interactions between such systems and users can be limited as human users prefer to not be overly bothered by such systems. As such, a key challenge is for the system to efficiently elicit key preferences without bothering the users too much. To tackle this problem, we propose a cost model that captures the cognitive or bother cost associated with asking a question. We incorporate this model into our iPLEASE system, an interactive preference elicitation approach. iPLEASE represents a user's preferences as a matrix, called preference matrix, and uses heuristics to select, from a given set of questions, an efficient sequence of questions to ask the user such that the total bother cost incurred to the user does not exceed a given bother cost budget. The user's response to those questions will partially populate the preference matrix. It then performs an exact matrix completion via convex optimization to approximate the remaining preferences that are not directly elicited. We empirically apply iPLEASE on randomly-generated problems as well as on a real-world dataset for the smart device scheduling problem to demonstrate that our approach outperforms other non-trivial benchmarks in eliciting user preferences.

Matrix completion, Preference elicitation, User bother cost
1459-1467
International Foundation for Autonomous Agents and Multiagent Systems
Le, Tiep
fd0b832a-1b67-4713-9e0e-4d7848ec4abe
Tabakhi, Atena M.
201fea9a-fcd7-456d-9c81-6edb1333dbef
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Yeoh, William
5a1add31-ad89-4196-9812-3d7fd2493389
Son, Tran Cao
0ca39d28-c7e8-4a92-9350-181c8b46d126
Le, Tiep
fd0b832a-1b67-4713-9e0e-4d7848ec4abe
Tabakhi, Atena M.
201fea9a-fcd7-456d-9c81-6edb1333dbef
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Yeoh, William
5a1add31-ad89-4196-9812-3d7fd2493389
Son, Tran Cao
0ca39d28-c7e8-4a92-9350-181c8b46d126

Le, Tiep, Tabakhi, Atena M., Tran-Thanh, Long, Yeoh, William and Son, Tran Cao (2018) Preference elicitation with interdependency and user bother cost. In 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018. vol. 2, International Foundation for Autonomous Agents and Multiagent Systems. pp. 1459-1467 .

Record type: Conference or Workshop Item (Paper)

Abstract

Agent-based scheduling systems, such as automated systems that schedule meetings for users and systems that schedule smart devices in smart homes, require the elicitation of user preferences in oder to operate in a manner that is consistent with user expectations. Unfortunately, interactions between such systems and users can be limited as human users prefer to not be overly bothered by such systems. As such, a key challenge is for the system to efficiently elicit key preferences without bothering the users too much. To tackle this problem, we propose a cost model that captures the cognitive or bother cost associated with asking a question. We incorporate this model into our iPLEASE system, an interactive preference elicitation approach. iPLEASE represents a user's preferences as a matrix, called preference matrix, and uses heuristics to select, from a given set of questions, an efficient sequence of questions to ask the user such that the total bother cost incurred to the user does not exceed a given bother cost budget. The user's response to those questions will partially populate the preference matrix. It then performs an exact matrix completion via convex optimization to approximate the remaining preferences that are not directly elicited. We empirically apply iPLEASE on randomly-generated problems as well as on a real-world dataset for the smart device scheduling problem to demonstrate that our approach outperforms other non-trivial benchmarks in eliciting user preferences.

Full text not available from this repository.

More information

Published date: 2018
Venue - Dates: 17th International Conference on Autonomous Agents and Multiagent Systems, Stockholm, Sweden, 2018-07-11 - 2018-07-13
Keywords: Matrix completion, Preference elicitation, User bother cost

Identifiers

Local EPrints ID: 425572
URI: https://eprints.soton.ac.uk/id/eprint/425572
PURE UUID: 401083dc-fe19-433d-86e6-8e031b06524b
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

Catalogue record

Date deposited: 25 Oct 2018 16:30
Last modified: 14 Mar 2019 01:38

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