Browsing by Subject "Artificial Intelligence"
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Item Open Access An eager regression method based on best feature projections(Springer, Berlin, Heidelberg, 2001) Aydın, Tolga; Güvenir, H. AltayThis paper describes a machine learning method, called Regression by Selecting Best Feature Projections (RSBFP). In the training phase, RSBFP projects the training data on each feature dimension and aims to find the predictive power of each feature attribute by constructing simple linear regression lines, one per each continuous feature and number of categories per each categorical feature. Because, although the predictive power of a continuous feature is constant, it varies for each distinct value of categorical features. Then the simple linear regression lines are sorted according to their predictive power. In the querying phase of learning, the best linear regression line and thus the best feature projection are selected to make predictions. © Springer-Verlag Berlin Heidelberg 2001.Item Open Access Guest editor’s introduction(Springer, 1998) Akman, V.Item Open Access Learning problem solving strategies using refinement and macro generation(Elsevier BV, 1990) Güvenir, H. A.; Ernst, G. W.In this paper we propose a technique for learning efficient strategies for solving a certain class of problems. The method, RWM, makes use of two separate methods, namely, refinement and macro generation. The former is a method for partitioning a given problem into a sequence of easier subproblems. The latter is for efficiently learning composite moves which are useful in solving the problem. These methods and a system that incorporates them are described in detail. The kind of strategies learned by RWM are based on the GPS problem solving method. Examples of strategies learned for different types of problems are given. RWM has learned good strategies for some problems which are difficult by human standards. © 1990.Item Open Access A re-examination of computational creativity through non-human animals(2020-06) Ahmed, NashihaThe advancement of artificial intelligence suggests signs of computational creativity; however, the case for computational creativity is undermined by an anthropocentric bias. In this thesis, I attempt to broaden the theory of creativity in artificial intelligence by drawing observations from examples of non-human animal creativity. I also illustrate how this works in parallel with the 4E cognition theory. I conclude that addressing the underlying anthropocentric bias in evaluation of creativity in this way (1) allows a stronger case for computational creativity and (2) develops a more informed theory of creativity.Item Open Access Situated nonmonotonic temporal reasoning with BABY-SIT(IOS Press, 1997) Tın, E.; Akman, V.After a review of situation theory and previous attempts at 'computational' situation theory, we present a new programming environment, BABY-SIT, which is based on situation theory. We then demonstrate how problems requiring formal temporal reasoning can be solved in this framework. Specifically, the Yale Shooting Problem, which is commonly regarded as a canonical problem for nonmonotonic temporal reasoning, is implemented in BABY-SIT using Yoav Shoham's causal theories.Item Open Access Turing test: 50 years later(Springer, 2000) Saygin, A. P.; Cicekli, I.; Akman, V.The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the 'other minds' problem, and similar topics in philosophy of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test.We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic. © 2001 Kluwer Academic Publishers.Item Open Access Two learning approaches for protein name extraction(Academic Press, 2009) Tatar, S.; Cicekli, I.Protein name extraction, one of the basic tasks in automatic extraction of information from biological texts, remains challenging. In this paper, we explore the use of two different machine learning techniques and present the results of the conducted experiments. In the first method, Bigram language model is used to extract protein names. In the latter, we use an automatic rule learning method that can identify protein names located in the biological texts. In both cases, we generalize protein names by using hierarchically categorized syntactic token types. We conducted our experiments on two different datasets. Our first method based on Bigram language model achieved an F-score of 67.7% on the YAPEX dataset and 66.8% on the GENIA corpus. The developed rule learning method obtained 61.8% F-score value on the YAPEX dataset and 61.0% on the GENIA corpus. The results of the comparative experiments demonstrate that both techniques are applicable to the task of automatic protein name extraction, a prerequisite for the large-scale processing of biomedical literature. © 2009 Elsevier Inc. All rights reserved.