Abdul Rahim Nizamani

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Computable versions of Kolmogorov complexity have been used in the context of pattern discovery [1]. However, these complexity measures do not take the psychological dimension of pattern discovery into account. We propose a method for pattern discovery based on a version of Kolmogorov complexity where computations are restricted to a cognitive model with(More)
We present the system O? that operates in arbitrary symbolic domains, including arithmetic, logic, and grammar. O? can start from scratch and learn the general laws of a domain from examples. The main learning mechanism is a formalization of Occam’s razor. Learning is facilitated by working within a cognitive model of bounded rationality. Computational(More)
We propose a method for generating comprehensible explanations in description logic. Such explanations could be of potential use for e.g. engineers, doctors, and users of the semantic web. Users commonly need to understand why a logical statement follows from a set of hypotheses. Then, automatically generated explanations that are easily understandable(More)
We present a multi-domain computational model for symbolic reasoning that was designed with the aim of matching human performance. The computational model is able to reason by deduction, induction, and abduction. It begins with an arbitrary theory in a given domain and gradually extends this theory as new regularities are learned from positive and negative(More)
When Alice in Wonderland fell down the rabbit hole, she entered a world that was completely new to her. She gradually explored that world by observing, learning, and reasoning. This paper presents a simple system ALICE IN WONDERLAND that operates analogously. We model Alice’s Wonderland via a general notion of domain and Alice herself with a computational(More)
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