BDI vs FSM Agents in Social Simulations for Raising Awareness in Disasters: A Case Study in Melbourne Bushfires

BDI vs FSM Agents in Social Simulations for Raising Awareness in Disasters: A Case Study in Melbourne Bushfires

Carole Adam (Grenoble Informatics Lab (LIG), University Grenoble Alpes, Grenoble, France), Patrick Taillandier (MIAT, University Toulouse, Toulouse, France), Julie Dugdale (Grenoble Informatics Lab (LIG), University Grenoble Alps, Grenoble, France) and Benoit Gaudou (University Toulouse 1 Capitole, Toulouse, France)
DOI: 10.4018/IJISCRAM.2017010103
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Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Agent-based simulation is a powerful tool to test various management strategies on a simulated population, and to raise awareness of the actual population behaviour. But valid results depend on realistic underlying models. This article describes two simulations of the Australian population's behaviour during bushfires designed in previous work, one based on a finite-state machine architecture, the other based on a belief-desire-intention agent architecture. It then proposes several contributions towards more realistic agent-based models of human behaviour: a methodology and tool for easily designing BDI models; a number of objective and subjective criteria for comparing agent-based models; a comparison of our two models along these criteria, showing that BDI provides better explanability and understandability of behaviour, makes models easier to extend, and is therefore best adapted; and a discussion of possible extensions of BDI models to further improve their realism.
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Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Societies can manage such crisis and emergency situations in several ways: adopt urban and territory planning policies to reduce the risks (e.g. forbid construction in exposed areas); raise awareness and prepare the population in advance; or create efficient emergency management policies to deal with crises when they happen. Modelling and simulation offer tools to test the effects and complex interactions of these different strategies without waiting for an actual crisis to happen, without putting human lives at risk, with limited cost, and with a great degree of control on all conditions and the possibility of reproducing exactly the same situation as many times as needed.

When modelling human behaviour, mathematical, equation-based models are too limited (Parunak, Savit, & Riolo, 1998) whereas agent-based models offer many benefits (Bonabeau, 2002). They allow capturing emergent phenomena that characterise such complex systems; they provide an intuitive and realistic description of their behaviour; and they are flexible, offering different levels of abstraction by varying the complexity of agents. Agent-based modelling and simulation platforms over various architectures of different complexity for the agents: reflex or reactive agents are very simplistic, reacting to environ-mental stimuli without any long-term reasoning; finite-state machines require scripting all of the possible states of the agents and the corresponding transitions; cognitive agents offer a more flexible description of behaviours in terms of goals and plans.

In particular the BDI (Belief, Desire, Intention (Rao & George, 1991)) architecture is very sophisticated and realistic, grounded in the philosophy of human rationality (Bratman, 1987), and linked with emotions (Adam, Herzig, & Longin, 2009). Such realism of the human behaviour model is important for the simulation to produce valid results (van Ruijven, 2011). For these reasons and as previously discussed in (Adam & Gaudou, 2016a), the BDI architecture is therefore more adapted for crisis situations that involve complex individual decision-making, influenced by emotions (sometimes causing irrational actions), and by the social context (effect of group, family, etc.). According to (Norling, 2004), BDI also provides an adapted level of abstraction to describe human behaviour in terms of folk psychology, which is the preferred level of description for humans. It therefore addresses the problem of the scarcity of (quantitative) behavioural data by allowing the use of qualitative data such as witness statements or expert reports.

Despite these advantages that make it very suitable for social simulation, BDI has had limited use in this field due to the lack of adapted tools to harness its complexity (Adam & Gaudou, 2016a). In previous work (Adam, Danet, Thangarajah, & Dugdale, 2016; Adam, Beck, & Dugdale, 2015; P. Taillandier, Bourgais, Caillou, Adam, & Gaudou, 2016) we have described how two new tools could be used to de-velop BDI agent-based models from interviews. We illustrated how to use these tools by turning an existing model of the behaviour of the Australian population in bushfires (with a finite-state machine architecture (Adam & Gaudou, 2016b, 2017)) into a BDI model. In this paper we now want to com-pare these two models, addressing the same problem with different architectures, using both objective and subjective criteria. We believe that such model comparison is important to further justify the interest of BDI models.

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