Abstract
We present a novel framework that enables h-BD[I] agents to dynamically choose Its intention reconsideration policy in order to perform optimally in accordance with the dynamic changes in the environment. Paper discusses the present limitations of BDI (belief-desire-intention) agent model and proposes a new extended architecture, A-BD[I] for non deterministic, dynamic environments. The lack of learning competences and difficulties in dealing with vague or imprecise data sets in the environment are the main obstacles in finding an optimal solution in the present BDI model. Paper discuss the means-end reasoning of A-BD[I] agents in dynamic environments which execute plans for achieving the agent's intention.
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