Optimal Incremental Preference Elicitation during Negotiation
Authors: Tim Baarslag, Enrico H. Gerding
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness of our approach by combining our policy with well-known negotiation strategies and show that it significantly outperforms other elicitation strategies. As a second contribution, we show in an experimental setting that our elicitation method outperforms benchmark approaches when coupled with existing, well-known bidding strategies, regardless of the user elicitation costs. |
| Researcher Affiliation | Academia | Tim Baarslag and Enrico H. Gerding Agents, Interaction and Complexity Group University of Southampton SO17 1BJ, Southampton, UK {T.Baarslag, eg}@soton.ac.uk |
| Pseudocode | Yes | Algorithm 1: A generic negotiation strategy. Algorithm 2: Using Pandora s Rule to formulate an optimal elicitation strategy. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper describes generating 200 different negotiation scenarios for its experiments but does not provide concrete access information (link, DOI, formal citation) to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes the setup of its experiments using generated negotiation scenarios but does not specify training, validation, and test dataset splits in the traditional sense. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For our experiments, the agent exchanges bids with the opponent using the alternating offers protocol [Osborne and Rubinstein, 1994] with a deadline of N = 10 and N = 100 rounds. We set Pmin = r so that the aspiration threshold reaches the reservation value at the deadline, and we set Pmax to 1/2, which is a reasonable choice for the majority of elicitation costs. Lastly, we select three types of time-dependent tactics to define the aspiration threshold: Boulware (e = 5), Linear (e = 1), and Conceder (e = 1/5). |