Integrating Learning into a BDI Agent for Environments with Changing Dynamics

We propose a framework that adds learning for improving plan selection in the popular BDI agent programming paradigm. In contrast with previous proposals, the approach given here is able to scale up well with the complexity of the agent's plan library. Technically, we develop a novel confidence measure which allows the agent to adjust its reliance on the learning dynamically, facilitating in principle infinitely many (re)learning phases. We demonstrate the benefits of the approach in an example controller for energy management

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

An agent-based approach to assess drivers’ 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 drivers’ 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

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

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, 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., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ 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

Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving

Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework allows the development of BDI agents having both explicitly programmed plans and plans learned by the agent using RL. The two kinds of plans are seamlessly integrated and can be used without differences. Here, we take autonomous driving as a case study to verify the advantages of the proposed approach and framework. The BDI agent has hard-coded plans that define high-level directions while fine-grained navigation is learned by trial and error. This approach – compared to plain RL – is encouraging as RL struggles in temporally extended planning. We defined and trained an agent able to drive in a track with an intersection, at which it has to choose the correct path to reach the assigned target. A first step towards porting the system in the real-world has been done by building a 1/10 scale racecar prototype which learned how to drive in a simple track

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

Role Assignment Adaptation: An Intentional Forgetting Approach

In organizations the distribution of tasks is a rising challenge in complex and dynamic environments. By structuring responsibilities and expectations for task processing in roles, organizations provide a transparent approach for collaboration. However, if tasks are being generated unexpectedly, actors who enact multiple roles might be overloaded in dynamic environments. By focusing on relevant information in terms of an intentional forgetting mechanism, actors could overcome these overload situations. Therefore, we provide an agent-based simulation to model and analyze effects of intentional forgetting by adapting role assignments in dynamic environments. The agent architecture utilizes separated revision functions to control an agent’s perception and belief acquisition to focus on relevant information. The model is tested using a case-study in a simulated emergency response scenario. The simulation results show that adapting role assignments at runtime improves team performance significantly