Abstract: The lack of learning competencies and difficulties in dealing with
vague or imprecise data sets in the environment are the main obstacles to
finding an optimal solution in the present belief-desire-intention (BDI) model.
We present a new "intelligent-Deliberation" process in the hybrid
belief-desire-intention (h-BD[I]) architecture that enables improved decision
making features in a dynamic, and complex environment. Observation and
prediction of future effects and the results of the previous plan executions
are analyzed in the intention reconsideration process of our model. The forward
thinking ability of the agent is improved with the introduction of Temporal
Difference (TD) learning in reinforcement learning. An Adaptive Neuro Fuzzy
Inference System (ANFIS) is proposed for improved decision making in the
intention reconsideration process. A modified version called the Shared
Learning Vector Quantization (SLVQ) of the existing neural network based
learning vector quantification algorithm has been proposed to handle the agent
deliberation called. The paper also discusses the improved behaviour of the
agent deliberation process with the introduction of the SLVQ.