Keywords
agent-based modelling, bdi architecture, belief theory, cropping plan decision-making
Start Date
1-7-2012 12:00 AM
Abstract
Agent-based simulations are now widely used to study complex systems. However, the problem of the agent design is still an open issue, especially for social-ecological models, where some of the agents represent human beings. In fact, designing complex agents able to act in a believable way is a difficult task, in particular when their behaviour is led by many conflicting needs and desires. A widely used way to formalise the internal architecture of such complex agents is the BDI (Belief-Desire-Intention) paradigm. This paradigm allows to design expressive and realistic agents, yet, it is rarely used in simulation context. A reason is that most agent architectures based on the BDI paradigm are complex to understand by non-computer-scientists. Moreover, they are often very timeconsuming in terms of computation. In this paper, we propose a new architecture based on the BDI paradigm that copes with these two issues. In our architecture, the choice of the most relevant action by an agent is based on the belief theory. We present an application of our agent architecture to an actual model dedicated to cropping plan decision-making. This application that takes into plays thousands of farmer agents shows promising results.
A new BDI agent architecture based on the belief theory. Application to the modelling of cropping plan decision-making
Agent-based simulations are now widely used to study complex systems. However, the problem of the agent design is still an open issue, especially for social-ecological models, where some of the agents represent human beings. In fact, designing complex agents able to act in a believable way is a difficult task, in particular when their behaviour is led by many conflicting needs and desires. A widely used way to formalise the internal architecture of such complex agents is the BDI (Belief-Desire-Intention) paradigm. This paradigm allows to design expressive and realistic agents, yet, it is rarely used in simulation context. A reason is that most agent architectures based on the BDI paradigm are complex to understand by non-computer-scientists. Moreover, they are often very timeconsuming in terms of computation. In this paper, we propose a new architecture based on the BDI paradigm that copes with these two issues. In our architecture, the choice of the most relevant action by an agent is based on the belief theory. We present an application of our agent architecture to an actual model dedicated to cropping plan decision-making. This application that takes into plays thousands of farmer agents shows promising results.
