Transforming Multi-agent Planning Into Single-Agent Planning Using Best-Cost Strategy

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.

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Decidability Results in First-Order Epistemic Planning

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/575 ◽

2020 ◽

Author(s):

Andrés Occhipinti Liberman ◽

Rasmus Kræmmer Rendsvig

Keyword(s):

Epistemic Logic ◽

Single Agent ◽

Higher Order ◽

Dynamic Epistemic Logic ◽

Existence Problem ◽

Independent Action ◽

First Order ◽

Multi Agent ◽

Action Representations ◽

Multi Agent Planning

Propositional Dynamic Epistemic Logic (DEL) provides an expressive framework for epistemic planning, but lacks desirable features that are standard in first-order planning languages (such as problem-independent action representations via action schemas). A recent epistemic planning formalism based on First-Order Dynamic Epistemic Logic (FODEL) combines the strengths of DEL (higher-order epistemics) with those of first-order languages (lifted representation), yielding benefits in terms of expressiveness and representational succinctness. This paper studies the plan existence problem for FODEL planning, showing that while the problem is generally undecidable, the cases of single-agent planning and multi-agent planning with non-modal preconditions are decidable.

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Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

Applied Sciences ◽

10.3390/app11114948 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4948

Author(s):

Lorenzo Canese ◽

Gian Carlo Cardarilli ◽

Luca Di Di Nunzio ◽

Rocco Fazzolari ◽

Daniele Giardino ◽

...

Keyword(s):

Reinforcement Learning ◽

Mathematical Models ◽

Learning Algorithms ◽

Single Agent ◽

Critical Issues ◽

Multi Agent ◽

Pros And Cons ◽

Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

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Temporal sampling annealing schemes for receding horizon multi-agent planning

Robotics and Autonomous Systems ◽

10.1016/j.robot.2021.103823 ◽

2021 ◽

pp. 103823

Author(s):

Aaron Ma ◽

Mike Ouimet ◽

Jorge Cortés

Keyword(s):

Receding Horizon ◽

Temporal Sampling ◽

Multi Agent ◽

Multi Agent Planning

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On-Demand Channel Bonding in Heterogeneous WLANs: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽

10.3390/s20102789 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2789 ◽

Cited By ~ 1

Author(s):

Hang Qi ◽

Hao Huang ◽

Zhiqun Hu ◽

Xiangming Wen ◽

Zhaoming Lu

Keyword(s):

Reinforcement Learning ◽

Transmission Rate ◽

Single Agent ◽

Time Of Day ◽

Action Space ◽

Traffic Load ◽

Traffic Demand ◽

Channel Bonding ◽

On Demand ◽

Multi Agent

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

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Multi-mobile agent itinerary planning algorithms for data gathering in wireless sensor networks: A review paper

International Journal of Distributed Sensor Networks ◽

10.1177/1550147716684841 ◽

2017 ◽

Vol 13 (1) ◽

pp. 155014771668484 ◽

Cited By ~ 10

Author(s):

Huthiafa Q Qadori ◽

Zuriati A Zulkarnain ◽

Zurina Mohd Hanapi ◽

Shamala Subramaniam

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Mobile Agent ◽

Mobile Agents ◽

Single Agent ◽

Data Gathering ◽

Optimal Number ◽

Wireless Sensor ◽

Itinerary Planning ◽

Multi Agent

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

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