22 research outputs found
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