Softgoal-based plan selection in model-driven BDI agents

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The belief-desire-intention (BDI) model is one of the most widely used for developing agents. One of its benefits is the flexibility of choosing among different plans to achieve a goal and, to leverage this benefit, a particular algorithm that makes this choice must be selected. Although many techniques have been proposed addressing the plan selection process -- as well as other aspects of BDI agents -- they require many customisations and adaptations to be used in particular applications, thus requiring expert knowledge to be adopted, which is a real barrier to the large-scale adoption of this kind of agent technology. We thus in this paper propose a model-driven approach that allows modelling agents based on an extended BDI model (inspired by the Tropos meta-model) and automatically generating source code that implements agents with the ability of selecting plans. As this plan selection process typically involves analysing plan side effects, e.g. cost of execution, in the context of current agent preferences, our approach uses a preference-based plan selection process over what we refer to as softgoals. This process relies on the multi-attribute utility theory, taking into account the uncertainty of plan outcomes. We evaluate our approach experimentally.