• Deepak Khemani

      Articles written in Sadhana

    • Planning in bridge with thematic actions

      Deepak Khemani

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      The task of planning in a dynamic and an uncertain domain is considerably more challenging than in domains traditionally adopted byai planning methods. Planning in real situations has to be a knowledge intensive process, particularly since it is not easy to predict all the effects of one’s actions. Contract bridge offers a domain in which many of the issues involved in real world problems can be addressed without having to make simplifications in representation. Planning in the game of bridge takes us away from the traditional search-based methods (like the alpha-beta procedure), which are applicable in complete-information games like chess. In this paper we look at how knowledge can be structured to plan for declarer play in bridge. This involves deploying known move combinations, triggered by patterns which are abstracted out of the input, and then assembling the structures into a workable plan. The results demonstrate the viability of the proposed concepts.

    • Neural networks for contract bridge bidding

      B Yegnanarayana Deepak Khemani Manish Sarkar

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      The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.

    • Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

      Nitin Kumar Dhiman Dipti Deodhare Deepak Khemani

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      This paper presents a survey of Simultaneous Localization And Mapping (SLAM) algorithms for unmanned ground robots. SLAM is the process of creating a map of the environment, sometimes unknown a priori, while at the same time localizing the robot in the same map. The map could be one of different types i.e. metrical, topological, hybrid or semantic. In this paper, the classification of algorithms is done in three classes: (i) Metric map generating approaches, (ii) Qualitative map generating approaches, and (iii) Hybrid map generating approaches. SLAM algorithms for both static and dynamic environments have been surveyed. The algorithms in each class are further divided based on the techniques used. The survey in this paper presents the current state-of-the-art methods, including important landmark works reported in the literature.

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