Agent-update Models

In dynamic epistemic logic (Van Ditmarsch et al., 2008) it is customary to use an action frame (Baltag and Moss, 2004; Baltag et al., 1998) to describe different views of a single action. In this article, action frames are extended to add or remove agents, we call these agent-update frames. This can be done selectively so that only some specified agents get information of the update, which can be used to model several interesting examples such as private update and deception, studied earlier by Baltag and Moss (2004); Sakama (2015); Van Ditmarsch et al. (2012). The product update of a Kripke model by an action frame is an abbreviated way of describing the transformed Kripke model which is the result of performing the action. This is substantially extended to a sum-product update of a Kripke model by an agent-update frame in the new setting. These ideas are applied to an AI problem of modelling a story. We show that dynamic epistemic logics, with update modalities now based on agent-update frames, continue to have sound and complete proof systems. Decision procedures for model checking and satisfiability have expected complexity. A sublanguage is shown to have polynomial space algorithms

Neural networks for contract bridge bidding

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

Unsupervised Learning from URL Corpora

This paper illustrates the utility of URL information in unsupervised learning. We outline the motivation behind the usage of URL information upfront, and present two techniques for unsupervised learning from URL corpora. First, we devise a similarity measure for URL pairs putting down the intuitions behind the same and verify its goodness by using it for clustering

Differential voting in case based spam filtering

Abstract. Case-based reasoning (CBR) has been shown to be of considerable utility in a spam-filtering task. In the course of this study, we propose that the non-random skewed distribution of the cases in a case base is crucial, especially in the context of a classification task like spam filtering. In this paper, we propose approaches to improve the performance of a CBR spam filter by making use of the non-random nature of the case base. We associate each case in the case base with a voting power, which is essentially a function that incorporates the knowledge of the local neighborhood of the case. We show that the performance of the spam filter can be considerably improved by making use of such techniques that incorporate the voting powers.