There are many challenges involved in the realization of a shopping assistance robot (SAR). The specific challenge addressed in this paper is that of incorporating artiticial intelligence or decision making capability in such robot. Markov Decision Process (MDP) based formulation of the problem has been presented for this purpose. The major advantage of the MDP based approach over simple search based artificial intelligence techniques is that it can incorporate uncertainty. The proposed MDP model has been solved for optimal policy using value iteration algorithm. Furthermore, it has been shown how the reward function influences the structure of the resulting policy. The results show encouraging potential in the use ofMDP based formulation for SAR.
Summary
Vertical handoff is a major concern in the operation of mobile connections. Multiple wireless networks collate to provide smooth and quality service to the users over mobile connections. This paper formulates a Markov decision process for handoff decisions with a sample space that includes a union of parameters that are important for making a handoff decision. The major contribution of this paper is to propose three different yet closely related algorithms for reducing the computational complexity of the original problem. In particular, we propose a feature‐wise assessment algorithm, a network‐wise assessment algorithm, and a hybrid approach for computational complexity reduction. Discussed algorithms give pseudo‐optimal solutions with a significant reduction in computational complexity. Results indicate that different complexity reduction algorithms perform best under different circumstances. This provides a guideline for the selection of complexity reduction algorithms based on real scenarios.
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