Beyond Rings: Gathering in 1-Interval Connected Graphs

2021 ◽  
Author(s):  
Othon Michail ◽  
Paul G. Spirakis ◽  
Michail Theofilatos
Keyword(s):  
Deterministic Algorithm ◽  
Dynamic Graphs ◽  
Connected Graphs ◽  
Underlying Graph ◽  
Spanning Subgraphs ◽  
Connectivity Model ◽  
Connectivity Property ◽  
Multi Agent ◽  
Initial Agent ◽  
Explicit Communication

We examine the problem of gathering [Formula: see text] agents (or multi-agent rendezvous) in dynamic graphs which may change in every round. We consider a variant of the [Formula: see text]-interval connectivity model [9] in which all instances (snapshots) are always connected spanning subgraphs of an underlying graph, not necessarily a clique. The agents are identical and not equipped with explicit communication capabilities, and are initially arbitrarily positioned on the graph. The problem is for the agents to gather at the same node, not fixed in advance. We first show that the problem becomes impossible to solve if the underlying graph has a cycle. In light of this, we study a relaxed version of this problem, called weak gathering, where the agents are allowed to gather either at the same node, or at two adjacent nodes. Our goal is to characterize the class of 1-interval connected graphs and initial configurations in which the problem is solvable, both with and without homebases. On the negative side we show that when the underlying graph contains a spanning bicyclic subgraph and satisfies an additional connectivity property, weak gathering is unsolvable, thus we concentrate mainly on unicyclic graphs. As we show, in most instances of initial agent configurations, the agents must meet on the cycle. This adds an additional difficulty to the problem, as they need to explore the graph and recognize the nodes that form the cycle. We provide a deterministic algorithm for the solvable cases of this problem that runs in [Formula: see text] number of rounds.

scholarly journals Adaptive Load Balancing: A Study in Multi-Agent Learning

1995 ◽  
Vol 2 ◽  
pp. 475-500 ◽  
Author(s):  
A. Schaerf ◽  
Y. Shoham ◽  
M. Tennenholtz
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Distributed System ◽  
Adaptive Behavior ◽  
Local Information ◽  
System Efficiency ◽  
Agent Learning ◽  
Multi Agent ◽  
Explicit Communication

We study the process of multi-agent reinforcement learning in the context ofload balancing in a distributed system, without use of either centralcoordination or explicit communication. We first define a precise frameworkin which to study adaptive load balancing, important features of which are itsstochastic nature and the purely local information available to individualagents. Given this framework, we show illuminating results on the interplaybetween basic adaptive behavior parameters and their effect on systemefficiency. We then investigate the properties of adaptive load balancing inheterogeneous populations, and address the issue of exploration vs.exploitation in that context. Finally, we show that naive use ofcommunication may not improve, and might even harm system efficiency.

scholarly journals The elaboration of a manufacturing flow connectivity model, based on Multi Agent System

2017 ◽  
Vol 105 ◽  
pp. 00011 ◽  
Author(s):  
Lamyae Fahhama ◽  
Abdellah Zamma ◽  
Khalifa Mansouri ◽  
Zayer Elmajid
Keyword(s):  
Multi Agent System ◽  
Agent System ◽  
Model Based ◽  
Connectivity Model ◽  
Multi Agent

scholarly journals Quantum symmetry of graph C∗-algebras associated with connected graphs

2018 ◽  
Vol 21 (03) ◽  
pp. 1850019 ◽  
Author(s):  
Soumalya Joardar ◽  
Arnab Mandal
Keyword(s):  
Automorphism Group ◽  
Directed Graph ◽  
Automorphism Groups ◽  
Multiple Edge ◽  
Connected Graphs ◽  
C Algebras ◽  
Underlying Graph ◽  
Quantum Symmetry ◽  
Quantum Symmetries ◽  
Funct Anal

We define a notion of quantum automorphism groups of graph [Formula: see text]-algebras for finite, connected graphs. Under the assumption that the underlying graph does not have any multiple edge or loop, the quantum automorphism group of the underlying directed graph in the sense of Banica [Quantum automorphism groups of homogeneous graphs, J. Funct. Anal. 224 (2005) 243–280] (which is also the symmetry object in the sense of [S. Schmidt and M. Weber, Quantum symmetry of graph [Formula: see text]-algebras, arXiv:1706.08833 ] is shown to be a quantum subgroup of quantum automorphism group in our sense. Quantum symmetries for some concrete graph [Formula: see text]-algebras have been computed.

scholarly journals Learning to Communicate Implicitly by Actions

2020 ◽  
Vol 34 (05) ◽  
pp. 7261-7268
Author(s):  
Zheng Tian ◽  
Shihao Zou ◽  
Ian Davies ◽  
Tim Warr ◽  
Lisheng Wu ◽  
...  
Keyword(s):  
Learning Process ◽  
Private Information ◽  
Competitive Bidding ◽  
Matrix Game ◽  
Multi Agent Systems ◽  
Belief Learning ◽  
Multi Agent ◽  
Contract Bridge ◽  
Successful Collaboration ◽  
Explicit Communication

In situations where explicit communication is limited, human collaborators act by learning to: (i) infer meaning behind their partner's actions, and (ii) convey private information about the state to their partner implicitly through actions. The first component of this learning process has been well-studied in multi-agent systems, whereas the second — which is equally crucial for successful collaboration — has not. To mimic both components mentioned above, thereby completing the learning process, we introduce a novel algorithm: Policy Belief Learning (PBL). PBL uses a belief module to model the other agent's private information and a policy module to form a distribution over actions informed by the belief module. Furthermore, to encourage communication by actions, we propose a novel auxiliary reward which incentivizes one agent to help its partner to make correct inferences about its private information. The auxiliary reward for communication is integrated into the learning of the policy module. We evaluate our approach on a set of environments including a matrix game, particle environment and the non-competitive bidding problem from contract bridge. We show empirically that this auxiliary reward is effective and easy to generalize. These results demonstrate that our PBL algorithm can produce strong pairs of agents in collaborative games where explicit communication is disabled.

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