1,989 research outputs found
An agent-based approach to assess driversâ interaction with pre-trip information systems.
This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing driversâ
responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents,
and travel demand results from the decision-making deliberation performed by each individual of the population as regards
route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users
on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the
overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand
formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the
one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing
us to represent many of the mental aspects involved in the deliberation process
A multi-agent system with application in project scheduling
The new economic and social dynamics increase project complexity and makes scheduling problems more difficult, therefore scheduling requires more versatile solutions as Multi Agent Systems (MAS). In this paper the authors analyze the implementation of a Multi-Agent System (MAS) considering two scheduling problems: TCPSP (Time-Constrained Project Scheduling), and RCPSP (Resource-Constrained Project Scheduling). The authors propose an improved BDI (Beliefs, Desires, and Intentions) model and present the first the MAS implementation results in JADE platform.multi-agent architecture, scheduling, project management, BDI architecture, JADE.
Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture
The embedding of self-organizing inter-agent processes in distributed
software applications enables the decentralized coordination system elements,
solely based on concerted, localized interactions. The separation and
encapsulation of the activities that are conceptually related to the
coordination, is a crucial concern for systematic development practices in
order to prepare the reuse and systematic integration of coordination processes
in software systems. Here, we discuss a programming model that is based on the
externalization of processes prescriptions and their embedding in Multi-Agent
Systems (MAS). One fundamental design concern for a corresponding execution
middleware is the minimal-invasive augmentation of the activities that affect
coordination. This design challenge is approached by the activation of agent
modules. Modules are converted to software elements that reason about and
modify their host agent. We discuss and formalize this extension within the
context of a generic coordination architecture and exemplify the proposed
programming model with the decentralized management of (web) service
infrastructures
Integrating BDI agents with Agent-based simulation platforms
Agent-Based Models (ABMs) is increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is exible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Using MATSim as a Component in Dynamic Agent-Based Micro-Simulations
This paper discusses use of the widely used transport simulator, MATSim, as one component in a large complex agent based microsimulation where dynamic changes in the environment require the agents to be reactive as well as goal directed. We describe a number of refinements to MATSim that have been made to facilitate its use within our deployed wildfire evacuation applications, as well as some tools that have been developed which complement MATSim. All code is freely available under open source licenses. As applications increasingly require complex microsimulations, with many aspects, it is important to use existing software where possible. However most simulation systems, like MATSim, have been developed as standalone systems. We identify ways that MATSim has needed to be extended or modified in order for it to be used as a component in a larger whole. The paper provides details that will be useful for anyone wanting to use MATSim within their specific application
Agent programming in the cognitive era
It is claimed that, in the nascent âCognitive Eraâ, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., âAI as a serviceâ, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., âAI embedded into agentsâ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs
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