$${\upvarepsilon }$$-Strong Privacy Preserving Multi-agent Planning

2018 ◽  
pp. 137-156 ◽  
Author(s):  
Antonín Komenda ◽  
Jan Tožička ◽  
Michal Štolba
Keyword(s):  
Privacy Preserving ◽  
Multi Agent ◽  
Multi Agent Planning

Action dependencies in privacy-preserving multi-agent planning

2018 ◽  
Vol 32 (6) ◽  
pp. 779-821
Author(s):  
Shlomi Maliah ◽  
Guy Shani ◽  
Roni Stern
Keyword(s):  
Privacy Preserving ◽  
Multi Agent ◽  
Multi Agent Planning

Collaborative privacy preserving multi-agent planning

2016 ◽  
Vol 31 (3) ◽  
pp. 493-530 ◽  
Author(s):  
Shlomi Maliah ◽  
Guy Shani ◽  
Roni Stern
Keyword(s):  
Privacy Preserving ◽  
Multi Agent ◽  
Multi Agent Planning

scholarly journals Distributed Heuristic Forward Search for Multi-agent Planning

2014 ◽  
Vol 51 ◽  
pp. 293-332 ◽  
Author(s):  
R. Nissim ◽  
R. Brafman
Keyword(s):  
Partial Order ◽  
Private Information ◽  
Search Algorithm ◽  
Single Agent ◽  
Privacy Preserving ◽  
Forward Search ◽  
Planning Techniques ◽  
State Expansion ◽  
Multi Agent ◽  
Multi Agent Planning

This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of problem -- one is based on reduction to distributed constraint satisfaction, and the other on partial-order planning techniques. In classical single-agent planning, constraint-based and partial-order planning techniques are currently dominated by heuristic forward search. The question arises whether it is possible to formulate a distributed heuristic forward search algorithm for privacy-preserving classical multi-agent planning. Our work provides a positive answer to this question in the form of a general approach to distributed state-space search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient -- outperforming previous algorithms by orders of magnitude -- while offering similar flexibility to that of forward-search algorithms for single-agent planning. Furthermore, one particular variant of our general approach yields a distributed version of the A* algorithm that is the first cost-optimal distributed algorithm for privacy-preserving planning.

scholarly journals Advances and Challenges in Privacy Preserving Planning

2018 ◽  
Author(s):  
Guy Shani
Keyword(s):  
Private Information ◽  
Planning Process ◽  
Privacy Preserving ◽  
Search Process ◽  
Local Planning ◽  
Open Problems ◽  
Future Directions ◽  
Multi Agent ◽  
Global Coordination ◽  
Multi Agent Planning

Collaborative privacy-preserving planning (CPPP) is a multi-agent planning task in which agents need to achieve a common set of goals without revealing certain private information. CPPP has gained attention in recent years as an important sub area of multi agent planning, presenting new challenges to the planning community. In this paper we describe recent advancements, and outline open problems and future directions in this field. We begin with describing different models of privacy, such as weak and strong privacy, agent privacy, and cardinality preserving privacy. We then discuss different solution approaches, focusing on the two prominent methods --- joint creation of a global coordination scheme first, followed by independent planning to extend the global scheme with private actions; and collaborative local planning where agents communicate information concerning their planning process. In both cases a heuristic is needed to guide the search process. We describe several adaptations of well known classical planning heuristic to CPPP, focusing on the difficulties in computing the heuristic without disclosing private information.

Privacy-preserving weighted average consensus and optimal attacking strategy for multi-agent networks

2021 ◽  
Vol 358 (6) ◽  
pp. 3033-3050
Author(s):  
Aijuan Wang ◽  
Wanping Liu ◽  
Tiehu Li ◽  
Tingwen Huang
Keyword(s):  
Weighted Average ◽  
Privacy Preserving ◽  
Average Consensus ◽  
Multi Agent ◽  
Agent Networks

scholarly journals A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Farzaneh Farhadi ◽  
Nicholas R. Jennings
Keyword(s):  
Information Leakage ◽  
Optimal Strategies ◽  
Privacy Preserving ◽  
Limited Attention ◽  
Limited Information ◽  
Multi Agent Systems ◽  
Negotiation Strategies ◽  
Multi Agent ◽  
Theoretical And Numerical Analysis ◽  
Negotiation Mechanism

AbstractDistributed multi-agent agreement problems (MAPs) are central to many multi-agent systems. However, to date, the issues associated with encounters between self-interested and privacy-preserving agents have received limited attention. Given this, we develop the first distributed negotiation mechanism that enables self-interested agents to reach a socially desirable agreement with limited information leakage. The agents’ optimal negotiation strategies in this mechanism are investigated. Specifically, we propose a reinforcement learning-based approach to train agents to learn their optimal strategies in the proposed mechanism. Also, a heuristic algorithm is designed to find close-to-optimal negotiation strategies with reduced computational costs. We demonstrate the effectiveness and strength of our proposed mechanism through both game theoretical and numerical analysis. We prove theoretically that the proposed mechanism is budget balanced and motivates the agents to participate and follow the rules faithfully. The experimental results confirm that the proposed mechanism significantly outperforms the current state of the art, by increasing the social-welfare and decreasing the privacy leakage.

Realization of multi-agent planning system for autonomous spacecraft

2005 ◽  
Vol 36 (4) ◽  
pp. 266-272 ◽  
Author(s):  
Xu Rui ◽  
Cui Pingyuan ◽  
Xu Xiaofei
Keyword(s):  
Planning System ◽  
Multi Agent ◽  
Multi Agent Planning
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