{"title":"A family of strategyproof mechanisms for activity scheduling","authors":"Xinping Xu, Jingwen Zhang, Minming Li, Lingjie Duan, Lihua Xie","doi":"10.1007/s10458-023-09624-7","DOIUrl":null,"url":null,"abstract":"<div><p>Recent years have seen various designs of strategyproof mechanisms in the facility location game and the obnoxious facility game, by considering the facility’s geo-location as a point in the spatial domain. In this paper, we extend this point to be a continuous interval, and study a novel activity scheduling game to schedule an activity in the normalized time domain [0, 1] based on all agents’ time reports for preferences/conflicts. The activity starts at time point <i>y</i> and lasts for a fixed time period of <i>d</i> with <span>\\(0\\le d\\le 1\\)</span>. Each agent <span>\\(i\\in N = \\{1, \\cdots , n\\}\\)</span> wants his preferred time interval <span>\\([t_i,t_i+l_i]\\)</span> to be close to or overlap with the activity interval <span>\\([y,y+d]\\)</span>. Since agents are heterogeneous, we consider each agent <i>i</i> has weight <span>\\(\\alpha _i\\)</span> or <span>\\(\\beta _i\\)</span> when the activity is scheduled after or before his time interval, respectively. Thus each agent <i>i</i>’s cost is his weight (<span>\\(\\alpha _i\\)</span> or <span>\\(\\beta _i\\)</span>) multiplied by the time difference between his time interval <span>\\([t_i,t_i+l_i]\\)</span> and the activity interval <span>\\([y,y+d].\\)</span> The social cost is the summation of all agents’ costs. In this game, agents’ preferred time intervals <span>\\([t_i,t_i+l_i]\\)</span>’s are private information and they may misreport such information to the social planner. Our objective is to choose the activity starting time <i>y</i> so that the mechanisms are strategyproof (i.e., all agents should be truthful to report <span>\\(t_i\\)</span>’s and <span>\\(l_i\\)</span>’s) and perform well with respect to minimizing the social cost. We design a mechanism outputting an optimal solution and prove that it is group strategyproof. For the objective of minimizing the maximum cost among agents, we design another strategyproof mechanism with the approximation ratio <span>\\(1+\\min \\{\\alpha /\\beta ,\\beta /\\alpha \\}\\)</span> when <span>\\(\\alpha _i=\\alpha , \\beta _i = \\beta\\)</span> for <span>\\(i\\in N,\\)</span> and prove it is the best strategyproof mechanism. In the obnoxious activity scheduling game, each agent prefers his conflicting time interval <span>\\([t_i,t_i+l_i]\\)</span> to be far away from the activity interval <span>\\([y,y+d]\\)</span>. We design deterministic and randomized group strategyproof mechanisms, and compare their provable approximation ratios to the lower bounds. Finally, we consider the cost/utility of each agent as a 0-1 indicator function and find group strategyproof mechanisms for minimizing the social cost and maximizing the social utility.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"37 2","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autonomous Agents and Multi-Agent Systems","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10458-023-09624-7","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Recent years have seen various designs of strategyproof mechanisms in the facility location game and the obnoxious facility game, by considering the facility’s geo-location as a point in the spatial domain. In this paper, we extend this point to be a continuous interval, and study a novel activity scheduling game to schedule an activity in the normalized time domain [0, 1] based on all agents’ time reports for preferences/conflicts. The activity starts at time point y and lasts for a fixed time period of d with \(0\le d\le 1\). Each agent \(i\in N = \{1, \cdots , n\}\) wants his preferred time interval \([t_i,t_i+l_i]\) to be close to or overlap with the activity interval \([y,y+d]\). Since agents are heterogeneous, we consider each agent i has weight \(\alpha _i\) or \(\beta _i\) when the activity is scheduled after or before his time interval, respectively. Thus each agent i’s cost is his weight (\(\alpha _i\) or \(\beta _i\)) multiplied by the time difference between his time interval \([t_i,t_i+l_i]\) and the activity interval \([y,y+d].\) The social cost is the summation of all agents’ costs. In this game, agents’ preferred time intervals \([t_i,t_i+l_i]\)’s are private information and they may misreport such information to the social planner. Our objective is to choose the activity starting time y so that the mechanisms are strategyproof (i.e., all agents should be truthful to report \(t_i\)’s and \(l_i\)’s) and perform well with respect to minimizing the social cost. We design a mechanism outputting an optimal solution and prove that it is group strategyproof. For the objective of minimizing the maximum cost among agents, we design another strategyproof mechanism with the approximation ratio \(1+\min \{\alpha /\beta ,\beta /\alpha \}\) when \(\alpha _i=\alpha , \beta _i = \beta\) for \(i\in N,\) and prove it is the best strategyproof mechanism. In the obnoxious activity scheduling game, each agent prefers his conflicting time interval \([t_i,t_i+l_i]\) to be far away from the activity interval \([y,y+d]\). We design deterministic and randomized group strategyproof mechanisms, and compare their provable approximation ratios to the lower bounds. Finally, we consider the cost/utility of each agent as a 0-1 indicator function and find group strategyproof mechanisms for minimizing the social cost and maximizing the social utility.
期刊介绍:
This is the official journal of the International Foundation for Autonomous Agents and Multi-Agent Systems. It provides a leading forum for disseminating significant original research results in the foundations, theory, development, analysis, and applications of autonomous agents and multi-agent systems. Coverage in Autonomous Agents and Multi-Agent Systems includes, but is not limited to:
Agent decision-making architectures and their evaluation, including: cognitive models; knowledge representation; logics for agency; ontological reasoning; planning (single and multi-agent); reasoning (single and multi-agent)
Cooperation and teamwork, including: distributed problem solving; human-robot/agent interaction; multi-user/multi-virtual-agent interaction; coalition formation; coordination
Agent communication languages, including: their semantics, pragmatics, and implementation; agent communication protocols and conversations; agent commitments; speech act theory
Ontologies for agent systems, agents and the semantic web, agents and semantic web services, Grid-based systems, and service-oriented computing
Agent societies and societal issues, including: artificial social systems; environments, organizations and institutions; ethical and legal issues; privacy, safety and security; trust, reliability and reputation
Agent-based system development, including: agent development techniques, tools and environments; agent programming languages; agent specification or validation languages
Agent-based simulation, including: emergent behavior; participatory simulation; simulation techniques, tools and environments; social simulation
Agreement technologies, including: argumentation; collective decision making; judgment aggregation and belief merging; negotiation; norms
Economic paradigms, including: auction and mechanism design; bargaining and negotiation; economically-motivated agents; game theory (cooperative and non-cooperative); social choice and voting
Learning agents, including: computational architectures for learning agents; evolution, adaptation; multi-agent learning.
Robotic agents, including: integrated perception, cognition, and action; cognitive robotics; robot planning (including action and motion planning); multi-robot systems.
Virtual agents, including: agents in games and virtual environments; companion and coaching agents; modeling personality, emotions; multimodal interaction; verbal and non-verbal expressiveness
Significant, novel applications of agent technology
Comprehensive reviews and authoritative tutorials of research and practice in agent systems
Comprehensive and authoritative reviews of books dealing with agents and multi-agent systems.