scholarly journals Quantum Contagion: A Quantum-Like Approach for the Analysis of Social Contagion Dynamics with Heterogeneous Adoption Thresholds

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 538
Author(s):  
Ece C. Mutlu ◽  
Ozlem Ozmen Garibay
Keyword(s):  
Information Science ◽  
Transmission Probability ◽  
Social Contagion ◽  
Multi Agent Systems ◽  
Adoption Decision ◽  
Novel Approach ◽  
Multi Agent ◽  
Edge Based ◽  
Regular Networks ◽  
Optimal Modeling

Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals’ uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.

INTENT RECOGNITION IN MULTI-AGENT SYSTEMS: COW HERDING

2014 ◽  
Vol 28 (05) ◽  
pp. 1459006
Author(s):  
NAJLA AHMAD ◽  
ARVIN AGAH
Keyword(s):  
Conceptual Framework ◽  
Recognition System ◽  
Plan Recognition ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Intent Recognition ◽  
Domain Specific ◽  
Novel Approach ◽  
Multi Agent

In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

scholarly journals Adaptive transmission probability for CSMA/CA-based consensus control of multi-agent systems

2019 ◽  
Vol 8 (9) ◽  
pp. 393-398 ◽  
Author(s):  
Kentaro Kobayashi ◽  
Shunsuke Noro ◽  
Hiraku Okada ◽  
Masaaki Katayama
Keyword(s):  
Transmission Probability ◽  
Adaptive Transmission ◽  
Multi Agent Systems ◽  
Consensus Control ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Counterplanning using Goal Recognition and Landmarks

2018 ◽  
Author(s):  
Alberto Pozanco ◽  
Yolanda E-Martín ◽  
Susana Fernández ◽  
Daniel Borrajo
Keyword(s):  
Automated Planning ◽  
Experimental Results ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent ◽  
Goal Recognition ◽  
Domain Independent

In non-cooperative multi-agent systems, agents might want to prevent the opponents from achieving their goals. One alternative to solve this task would be using counterplanning to generate a plan that allows an agent to block other's to reach their goals. In this paper, we introduce a fully automated domain-independent approach for counterplanning. It combines; goal recognition to infer an opponent's goal; landmarks' computation to identify subgoals that can be used to block opponents' goals achievement; and classical automated planning to generate plans that prevent the opponent's goals achievement. Experimental results in several domains show the benefits of our novel approach. 

scholarly journals A Novel Heterogeneous Swarm Reinforcement Learning Method for Sequential Decision Making Problems

2019 ◽  
Vol 1 (2) ◽  
pp. 590-610
Author(s):  
Zohreh Akbari ◽  
Rainer Unland
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Single Agent ◽  
Sequential Decision Making ◽  
Multi Agent Systems ◽  
Sequential Decision ◽  
Agent Systems ◽  
Novel Approach ◽  
Markov Decision ◽  
Multi Agent

Sequential Decision Making Problems (SDMPs) that can be modeled as Markov Decision Processes can be solved using methods that combine Dynamic Programming (DP) and Reinforcement Learning (RL). Depending on the problem scenarios and the available Decision Makers (DMs), such RL algorithms may be designed for single-agent systems or multi-agent systems that either consist of agents with individual goals and decision making capabilities, which are influenced by other agent’s decisions, or behave as a swarm of agents that collaboratively learn a single objective. Many studies have been conducted in this area; however, when concentrating on available swarm RL algorithms, one obtains a clear view of the areas that still require attention. Most of the studies in this area focus on homogeneous swarms and so far, systems introduced as Heterogeneous Swarms (HetSs) merely include very few, i.e., two or three sub-swarms of homogeneous agents, which either, according to their capabilities, deal with a specific sub-problem of the general problem or exhibit different behaviors in order to reduce the risk of bias. This study introduces a novel approach that allows agents, which are originally designed to solve different problems and hence have higher degrees of heterogeneity, to behave as a swarm when addressing identical sub-problems. In fact, the affinity between two agents, which measures the compatibility of agents to work together towards solving a specific sub-problem, is used in designing a Heterogeneous Swarm RL (HetSRL) algorithm that allows HetSs to solve the intended SDMPs.

Towards a New Approach for Controlling the Reorganization Process of Multi-Agent Systems

2014 ◽  
Vol 6 (2) ◽  
pp. 73-91
Author(s):  
Kalache Ayyoub ◽  
Farid Mokhati ◽  
Mourad Badri
Keyword(s):  
Control Process ◽  
Software Tool ◽  
Multi Agent Systems ◽  
Concrete Case ◽  
New Approach ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent ◽  
Computational Systems

Reorganization in Multi-Agent Systems plays a crucial role in the dynamic adaptation of the structure and the behaviour of organizations. In order to ensure consistency of the resulting organization, the reorganization process has to be controlled. This paper proposes a novel approach for controlling the reorganization process of Multi-Agent Systems, which are specified and implemented using the Framework OMACS (Organizational Model for Adaptive Computational Systems). The proposed control process is accomplished using the Framework MOP (Monitoring Oriented Programming) for supporting the verification of some reorganizational properties. The proposed approach, supported by a software tool that we developed, is illustrated using a concrete case study.

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