Performance Comparison of Simple Reflex Agents Using Stigmergy with Model-Based Agents in Self-Organizing Transportation

Schmid, Sebastian; Schraudner, Daniel; Harth, Andreas

doi:10.1109/acsos-c52956.2021.00071

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Even in the one case where we have polynomial-time tractability (Result C; homogeneous team design by library selection), intractability asserts itself when as few as two types of agents co-exist on a team (Result D). 5 That the various intractabilities we have demonstrated cannot be vanquished by invoking verification and design relative to teams with higher values of h, |Q|, and # ec suggests that we are seeing a tight frontier of tractability [18, Section 4.1] relative to the lowest possible values of these parameters. 6 As all of our results are derived relative to a simplified team operation model in which deterministic agents operate in a synchronous and discrete manner within a 2D grid-based environment, these results are not immediately applicable to probabilistic agents that operate in an asynchronous and continuous manner in the real world.…”

Section: Discussionmentioning

confidence: 99%

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Creating Teams of Simple Agents for Specified Tasks: A Computational Complexity Perspective

Wareham¹

2022

Preprint

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Teams of interacting and co-operating agents have been proposed as an efficient and robust alternative to monolithic centralized control for carrying out specified tasks in a variety of applications. A number of different team and agent architectures have been investigated, e.g., teams based on single vs multiple behaviorally-distinct types of agents (homogeneous vs heterogeneous teams), simple vs complex agents, direct vs indirect agent-to-agent communication. A consensus is emerging that (1) heterogeneous teams composed of simple agents that communicate indirectly are preferable and (2) automated methods for verifying and designing such teams are necessary. In this paper, we use computational complexity analysis to assess viable algorithmic options for such automated methods for various types of teams. Building on recent complexity analyses addressing related questions in swarm robotics, we prove that automated team verification and design are by large both exact and approximate polynomial-time intractable in general for the most basic types of homogeneous and heterogeneous teams consisting of simple agents that communicate indirectly. Our results suggest that tractability for these problems must be sought relative to additional restrictions on teams, agents, operating environments, and tasks.

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Section: Discussionmentioning

confidence: 99%

“…Based on the experience gained with various proof-of-concept experiments and implementations, the consensus is emerging that (1) heterogeneous teams composed of relatively simple agents that communicate indirectly are preferable [3,4,5] and (2) automated methods for verifying and designing such teams are necessary [6,7].…”

Section: Introductionmentioning

confidence: 99%

Creating Teams of Simple Agents for Specified Tasks: A Computational Complexity Perspective

Wareham¹

2022

Preprint

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“…Reflex agents include both model‐based and straightforward reflex agents. According to the comparison of several models in this article, simple reflex agents are preferred over model‐based ones for this work 9 . The i‐Agent (Intelligent Agent) is better compatible with contemporary technology, according to the authors Lu et al 10 who compared it to the prior mobile agent.…”

Section: Introductionmentioning

confidence: 99%

Healthcare security in cloud‐based wireless sensor networks: Botnet attack detection via autoencoder‐aided goal‐based artificial intelligent agent

Chellathurai Amirthabai,

Malhotra,

Thapasimuthu Rajeswari

et al. 2024

Concurrency and Computation

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SummaryThis paper presents a new method in the field of healthcare security that specifically targets cloud‐based wireless sensor networks (WSNs). The suggested method integrates a goal‐based artificial intelligent agent (GAIA) with an autoencoder (AE) architecture, yielding an autoencoder‐based agent (AE‐A). The main goal of this integrated system is to improve the efficiency of identifying botnet assaults, with a specific emphasis on the evolving security threats related to cloud computing. Our concept is around creating a meticulously calibrated, goal‐driven AI agent tailored explicitly for healthcare applications. The agent meticulously analyses network data and proficiently integrates autoencoder‐enhanced anomaly detection techniques to uncover intricate patterns indicative of botnet activities. The adaptability of the goal‐based AI agent is improved by ongoing real‐time learning, guaranteeing that its responses are in line with the primary goal of neutralizing threats. The autoencoder serves a vital role in the system by functioning as a tool for extracting features. This approach enables the AI Agent to navigate complex information and derive significant insights efficiently. Cloud computing resources greatly enhance the functionalities of a system, enabling scalability, real‐time analysis, and improved responsiveness. Utilizing goal‐driven AI and autoencoder together proves to be a successful strategy in safeguarding healthcare‐oriented WSNs against botnet attacks. This technique takes a proactive stance in ensuring the security of sensitive medical data. The suggested model is evaluated against various models, including the bidirectional long short‐term memory (BLSTM) method, the hybrid BLSTM with recurrent neural network (BLSTM‐RNN) algorithm, and the Random Forest algorithm. The models are evaluated using metrics such as Matthews correlation coefficient (MCC), prediction rate, accuracy, recall, precision, and F1 score analysis. The investigation demonstrates that the suggested model achieved the most significant values of 93% MCC, 94% prediction rate, 91% accuracy, 98% recall, 98% precision, and 98% F1 score, respectively when compared to the existing models.

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MOSAIK: An Agent-Based Decentralized Control System with Stigmergy for a Transportation Scenario

Schmid

Schraudner

Harth

2023

Lecture Notes in Computer Science

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Performance Comparison of Simple Reflex Agents Using Stigmergy with Model-Based Agents in Self-Organizing Transportation

Cited by 6 publications

References 23 publications

Creating Teams of Simple Agents for Specified Tasks: A Computational Complexity Perspective

Creating Teams of Simple Agents for Specified Tasks: A Computational Complexity Perspective

Healthcare security in cloud‐based wireless sensor networks: Botnet attack detection via autoencoder‐aided goal‐based artificial intelligent agent

MOSAIK: An Agent-Based Decentralized Control System with Stigmergy for a Transportation Scenario

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