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

Integrating BDI agents with agent-based simulation platforms

This paper describes an integration framework that allows development of simulations where the cognitive reasoning and decision making is programmed and executed within an existing BDI (Belief, Desire, Intention) system, and the simulation is played out in an existing ABM (Agent Based Modelling) system. The framework has a generic layer which manages communication and synchronisation, a system layer which integrates specific BDI and ABM systems, and the application layer which contains the program code for a particular application. The code is available on GitHub: https://github.com/agentsoz/bdi-abm-integratio

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

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

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

A Multi-Agent System framework to support the decision-making in complex real-world domains

The aim of this work was to develop a framework capable of supporting the decision-making process in complex real-world domains, such as environmental, industrial or medical domains using a Multi-Agent approach with Rule-based Reasoning. The validation of the framework was done in the environmental domain, particularly in the area of river basins

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

A Simple-to-Use BDI Architecture for Agent-Based Modeling and Simulation

With the increase of computing power and the development of user-friendly multi-agent simulation frameworks, social simulations have become increasingly realistic. However, most agent architectures in these simulations use simple reactive models. Cognitive architectures face two main obstacles: their complexity for the field-expert modeler, and their computational cost. In this paper, we propose a new cognitive agent architecture based on the Belief-Desire-Intention paradigm integrated into the GAMA modeling platform. Based on the GAML modeling language, this architecture was designed to be simple-to-use for modelers, flexible enough to manage complex behaviors, and with low computational cost. This architecture is illustrated with a simulation of the evolution of land-use in the Mekong Delta

Simulating Interactive Learning Scenarios with Intelligent Pedagogical Agents in a Virtual World through BDI-Based Agents

Intelligent Pedagogical Agents (IPAs) are designed for pedagogical purposes to support learning in 3D virtual learning environments. Several benefits of IPAs have been found adding to support learning effectiveness. Pedagogical agents can be thought of as a central point of interaction between the learner and the learning environment. And hence, the intelligent behavior and functional richness of pedagogical agents have the potential to reward back into increased engagement and learning effectiveness. However, the realization of those agents remains to be a challenge based on intelligent agents in virtual worlds. This paper reports the challenging reasons and most importantly an approach for simplification. A simulation based on BDI agents is introduced opening the road for several extensions and experimentation before implementation of IPAs in a virtual world can take place. The simulation provides a proof-of concept based on three intelligent agents to represent an IPA, a learner, and learning object implemented in JACK and Jadex intelligent agent platforms. To that end, the paper exhibits the difficulties, resolutions, and decisions made when designing and implementing the learning scenario in both domains of the virtual world and the agent-based simulation while comparing the two agent platforms

Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot