ARTIFICIAL INTELLIGENCE AND HUMAN THINKING AN INSIGHT REVIEW Amar Nath Singh and Er. Pratyusha Rajguru Department of Computer Science Gandhi Engineering College, Bhubaneswar ARTICLE INFO ABSTRACT Research in AI has built upon the tools and techniques of many different disciplines, including formal logic, probability theory, decision theory, management science, linguistics and philosophy[1]. However, the application of these disciplines in AI has necessitated the development of many enhancements and extensions. Among the most powerful of these are the methods of computational logic. I will argue that computational logic, embedded in an agent cycle, combines and improves upon both traditional logic and classical decision theory [1,2]. I will also argue that many of its methods can be used, not only in AI, but also in ordinary life, to help people improve their own human intelligence without the assistance of computers. INTRODUCTION Computational logic, like other kinds of logic, comes in many forms. In this paper, I will focus on the abductive logic programming (ALP) form of computational logic. I will argue that the ALP agent model, which embeds ALP in an agent cycle, is a powerful model of both descriptive and normative thinking. As a descriptive model, it includes production systems as a special case; and as a normative model, it includes classical logic and is compatible with classical decision theory. These descriptive and normative properties of the ALP agent model make it a dual process theory, which combines both intuitive and deliberative thinking. Like most theories, dual process theories also come in many forms[2]. But in one form, as Kahneman and Frederick [2002] put it, intuitive thinking “quickly proposes intuitive answers to judgement problems as they arise”, while deliberative thinking “monitors the quality of these proposals, which it may endorse, correct, or override”. In this paper, I will be concerned mainly with the normative features of the ALP agent model, and on ways in which it can help us to improve our own human thinking and behaviour. I will focus, in particular, on ways it can help us both to communicate more effectively with other people and to make better decisions in our lives[3]. I will argue that it provides a theoretical underpinning both for such guidelines on English writing style as [Williams, 1990, 1995], and for such advice on better decision-making as [Hammond et al.1999]. This paper is based upon [Kowalski, 2011], which contains the technical underpinnings of the ALP agent model, as well as references to related work.The schematic diagram is as showed below. A Brief Introduction to ALP Agents The ALP agent model can be viewed as a variant of the BDI model, in which agents use their beliefs to satisfy their desires by generating intentions, which are selected plans of actions. In ALP agents, beliefs and desires (or goals) areboth represented as conditionals in the clausal form of logic [4]. Beliefs are represented as logic programming clauses, and goals are represented as more general clauses, with the expressive power of full first-order logic (FOL). For example, the first sentence below expresses a goal, and the other four sentences express beliefs: International Journal of Current Advanced Research ISSN: O: 2319-6475, ISSN: P: 2319 – 6505, Impact Factor: SJIF: 5.995 Available Online at www.journalijcar.org Volume 6; Issue 7; July 2017; Page No. 4797-4801 DOI: http://dx.doi.org/10.24327/ijcar.2017.4801.0586 Article History: Received 18 th April, 2017 Received in revised form 14 th May, 2017 Accepted 20 th June, 2017 Published online 28 th July, 2017 Key words: Probability Theory, Decision Theory, Management Science Research Article Copyright©2017 Amar Nath Singh and Er. Pratyusha Rajguru. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. *Corresponding author: Amar Nath Singh Department of Computer Science Gandhi Engineering College, Bhubaneswar Fig-1, The basic ALP agent Lifecycle