Evidence Traces for Multi-agent Declarative Rule-Based Policy Simulation

2013 ◽  
Author(s):  
Ulf Lotzmann ◽  
Maria A. Wimmer
Keyword(s):  
Policy Simulation ◽  
Rule Based ◽  
Multi Agent

Rule Engines and Agent-Based Systems

2012 ◽  
pp. 211-218 ◽  
Author(s):  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Rule Engine ◽  
Rule Based ◽  
Agent Based ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Domain Specific ◽  
Multi Agent ◽  
Good Support

Expert systems are successfully applied to a number of domains. Often built on generic rule-based systems, they can also exploit optimized algorithms. On the other side, being based on loosely coupled components and peer to peer infrastructures for asynchronous messaging, multi-agent systems allow code mobility, adaptability, easy of deployment and reconfiguration, thus fitting distributed and dynamic environments. Also, they have good support for domain specific ontologies, an important feature when modelling human experts’ knowledge. The possibility of obtaining the best features of both technologies is concretely demonstrated by the integration of JBoss Rules, a rule engine efficiently implementing the Rete-OO algorithm, into JADE, a FIPA-compliant multi-agent system.

scholarly journals RULE RESPONDER: RULE-BASED AGENTS FOR THE SEMANTIC-PRAGMATIC WEB

2011 ◽  
Vol 20 (06) ◽  
pp. 1043-1081 ◽  
Author(s):  
ADRIAN PASCHKE ◽  
HAROLD BOLEY
Keyword(s):  
Distributed System ◽  
Autonomous Agents ◽  
Knowledge Bases ◽  
Virtual Organizations ◽  
Rule Based ◽  
Semantic Domain ◽  
Multi Agent ◽  
Web Infrastructure ◽  
Organizational Semiotics ◽  
Event Based

Rule Responder is a Pragmatic Web infrastructure for distributed rule-based event processing multi-agent eco-systems. This allows specifying virtual organizations — with their shared and individual (semantic and pragmatic) contexts, decisions, and actions/events for rule-based collaboration between the distributed members. The (semi-)autonomous agents use rule engines and Semantic Web rules to describe and execute derivation and reaction logic which declaratively implements the organizational semiotics and the different distributed system/agent topologies with their negotiation/coordination mechanisms. They employ ontologies in their knowledge bases to represent semantic domain vocabularies, normative pragmatics and pragmatic context of event-based conversations and actions.

Knowledge Acquisition of Self-organizing Systems with Deep Multiagent Reinforcement Learning

2021 ◽  
pp. 1-47
Author(s):  
Hao Ji ◽  
Yan Jin
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Acquisition ◽  
Learning Algorithm ◽  
Task Complexity ◽  
Functional Differentiation ◽  
Simple Task ◽  
Social Rules ◽  
Rule Based ◽  
Multi Agent ◽  
Self Organizing

Abstract Self-organizing systems (SOS) can perform complex tasks in unforeseen situations with adaptability. Previous work has introduced field-based approaches and rule-based social structuring for individual agents to not only comprehend the task situations but also take advantage of the social rule-based agent relations to accomplish their tasks without a centralized controller. Although the task fields and social rules can be predefined for relatively simple task situations, when the task complexity increases and the task environment changes, having a priori knowledge about these fields and the rules may not be feasible. In this paper, a multi-agent reinforcement learning based model is proposed as a design approach to solving the rule generation problem with complex SOS tasks. A deep multi-agent reinforcement learning algorithm was devised as a mechanism to train SOS agents for knowledge acquisition of the task field and social rules. Learning stability, functional differentiation and robustness properties of this learning approach were investigated with respect to the changing team sizes and task variations. Through computer simulation studies of a box-pushing problem, the results have shown that there is an optimal range of number of agents that achieves good learning stability; agents in a team learn to differentiate from other agents with changing team sizes and box dimensions; and the robustness of the learned knowledge shows to be stronger to the external noises than with changing task constraints.

scholarly journals Application of Multi-Agent Deep Reinforcement Learning to Optimize Real-World Traffic Signal Controls

2021 ◽  
Author(s):  
Maxim Friesen ◽  
Tian Tan ◽  
Jürgen Jasperneite ◽  
Jie Wang
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Traffic Congestion ◽  
Traffic Signal ◽  
Traffic Model ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Logic ◽  
Rule Based ◽  
Multi Agent

Increasing traffic congestion leads to significant costs associated by additional travel delays, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today's traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that could operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, most of these proposed approaches were validated using artificial traffic grids. This paper therefore presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, currently employed in the real world at the studied intersections, are integrated in the traffic model with LISA+ and serve as a performance baseline. Our performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach in LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.

scholarly journals Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving

2019 ◽  
Author(s):  
Akifumi Wachi
Keyword(s):  
Reinforcement Learning ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Development Phase ◽  
Rule Based ◽  
Safety Critical ◽  
Wide Range ◽  
Multi Agent ◽  
Failure Scenario

We examine the problem of adversarial reinforcement learning for multi-agent domains including a rule-based agent. Rule-based algorithms are required in safety-critical applications for them to work properly in a wide range of situations. Hence, every effort is made to find failure scenarios during the development phase. However, as the software becomes complicated, finding failure cases becomes difficult. Especially in multi-agent domains, such as autonomous driving environments, it is much harder to find useful failure scenarios that help us improve the algorithm. We propose a method for efficiently finding failure scenarios; this method trains the adversarial agents using multi-agent reinforcement learning such that the tested rule-based agent fails. We demonstrate the effectiveness of our proposed method using a simple environment and autonomous driving simulator.

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