Satisfying user preferences while negotiating meetings

Sen, Sandip; Haynes, Thomas; Arora, Neeraj

doi:10.1006/ijhc.1997.0139

Cited by 42 publications

(22 citation statements)

References 16 publications

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“…This method relies strongly on the cooperation of the agents in complying with this protocol. Sen et al (Sen, Haynes, & Arora 1997) also look at user preferences and show how voting methods can be used (within a software agent) to evaluate different options for scheduling a meeting when users have multi-faceted and possibly competing preferences. Garrido and Sycara (1995) and Jennings and Jackson (1995) take the approach of allowing agents to not only propose meeting times but also to quantify their preferences for proposals.…”

Section: Related Workmentioning

confidence: 99%

Learning to Select Negotiation Strategies in Multi-agent Meeting Scheduling

Crawford

Veloso

2005

Progress in Artificial Intelligence

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In this paper we look at the Multi-Agent Meeting Scheduling problem where distributed agents negotiate meeting times on behalf of their users. While many negotiation approaches have been proposed for scheduling meetings it is not well understood how agents can negotiate strategically in order to maximize their users' utility. To negotiate strategically an agent needs to learn to pick good strategies for each agent. We show how the playbook approach introduced by (Bowling, Browning, & Veloso 2004) for team plan selection in small-size robot soccer can be used to select strategies. Selecting strategies in this way gives some theoretical guarantees about regret. We also show experimental results demonstrating the effectiveness of the approach.

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Section: Related Workmentioning

confidence: 99%

Learning to Select Negotiation Strategies in Multi-agent Meeting Scheduling

Crawford

Veloso

2005

Progress in Artificial Intelligence

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“…This work is based on the communication protocol proposed by Sycara and Liu (Sycara and Liu, 1994), where agents are capable of negotiating and relaxing their constraints in order to reach an agreement on a schedule with high join utility. Another work also based on the multi-agent system, was described in (Sen et al, 1997). This work focuses on the problem of how an application domain for intelligent surrogate agents can be analyzed, understood and represented such that the underlying agents can make appropriate adaptations to their environment, to carry out tasks on behalf of human users.…”

Section: Introductionmentioning

confidence: 99%

“…This work focuses on the problem of how an application domain for intelligent surrogate agents can be analyzed, understood and represented such that the underlying agents can make appropriate adaptations to their environment, to carry out tasks on behalf of human users. The authors' prior work focused on agents adapting to environmental changes (Sen and Durfee, 1994), however Sen et al directed their efforts towards the integration of user preferences (Sen et al, 1997). Three other multi-agent approaches to MS problems, using the Partial CSP formalism introduced by (Freuder and Wallace, 1992), were given in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…However in real-world applications this is not always permitted. For example, when the user is traveling on business, such a constraint would oblige the user to stop his/her travel to attend the meeting, and this is not always possible (amongst Sycara and Liu, 1994;Garrido and Sycara, 1996;Sen et al, 1997;Luo et al, 2000;Tsuruta and Shintani, 2000;Franzin et al, 2004). (3) Not integrating the enforcement of local consistency in their solving process, in spite of the pre-eminent role of the filtering techniques in the efficiency of solving an NP-complete problem.…”

Section: Introductionmentioning

confidence: 99%

“…Especially in a dynamic environment, such discrimination may lead to conflicting meetings, and may also increase the number of meetings to reschedule. (5) Not considering the high complexity of message passing operations in real distributed systems (Garrido and Sycara, 1996;Sen et al, 1997;Luo et al, 2000;Tsuruta and Shintani, 2000;Franzin et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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An agent-based approach to solve dynamic meeting scheduling problems with preferences

Benhassine¹,

2007

Engineering Applications of Artificial Intelligence

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Multi-agent systems are widely used to address large-scale distributed combinatorial applications in the real world. One such application is meeting scheduling (MS), which is defined by a variety of features. The MS problem is naturally distributed and especially subject to many alterations. In addition, this problem is characterized by the presence of users' preferences that turn it into a search for an optimal rather than a feasible solution. However, in real-world applications users usually have conflicting preferences, which makes the solving process an NP-hard problem. Most research efforts in the literature, adopting agent-based technologies, tackle the MS problem as a static problem. They often share some common properties: allowing the relaxation of any user's time restriction, not dealing with achieving any level of consistency among meetings to enhance the efficiency of the solving process, not tackling the consequences of the dynamic environment, and especially not addressing the real difficulty of distributed systems which is the complexity of message passing operations.In an attempt to facilitate and streamline the process of scheduling meetings in any organization, the main contribution of this work is a new scalable agent-based approach for any dynamic MS problem (that we called MSRAC, for Meeting Scheduling with Reinforcement of Arc Consistency). In this approach we authorize only the relaxation of users' preferences while maintaining arc-consistency on the problem. The underlying protocol can efficiently reach the optimal solution (satisfying some predefined optimality criteria) whenever possible, using only minimum localized asynchronous communications. This purpose is achieved with minimal message passing while trying to preserve at most the privacy of involved users. Detailed experimental results on randomly generated MS problems show that MSRAC is scalable and it leads to speed up over other approaches, especially for large problems with strong constraints. r

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Software experience when using ontologies in a multi‐agent system for automated planning and scheduling

et al. 2006

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The main aim of this paper is to present the software experience when using multi‐agent systems (MASs) and ontologies in the development of a sample application. In particular, the authors have implemented a MAS for the planning and scheduling of a University Research Group. This MAS, called MASplan, should help group members to find the best possible time frames to hold a meeting and to designate the use of common resources. Copyright © 2006 John Wiley & Sons, Ltd.

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Satisfying user preferences while negotiating meetings

Cited by 42 publications

References 16 publications

Learning to Select Negotiation Strategies in Multi-agent Meeting Scheduling

Learning to Select Negotiation Strategies in Multi-agent Meeting Scheduling

An agent-based approach to solve dynamic meeting scheduling problems with preferences

Software experience when using ontologies in a multi‐agent system for automated planning and scheduling

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