International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 4 (sept-oct 2013), PP. 52-56 52 | Page FOUNDATIONS OF ARTIFICIAL INTELLIGENCE Sumit Kumar, Ashutosh Bhatt Computer Science and Engineering Department, Maharishi Dayanand University, Rohtak, Haryana, India Abstract —Artificial Intelligence was developed in 1956 and came into existence as a paradigm of cognition. It derived a powerful and lusty philosophical patrimony of functionalism and affirmatism. The history has shown a turn away from the functionalism of standard AI toward an alternative position that re-asserts the priority of development, interaction, and, more recently, emotion in cognitive systems, focusing now more than ever on enactive models of cognition. The method of looking for the solutions to problems, in Artificial Intelligence, can be brought about, in many ways, without cognition of the Domain, and in different situations, with knowledge of it. This procedure is usually called Heuristic Search. In such techniques matrix techniques reveal themselves as important. Their introduction can enable us to understand the precise way to the look for a solution. This paper explains the logical foundation of Artificial Intelligence with feasible applications. Index Terms—Artificial Intelligence, Fuzzy Logic, Cognition. (key words) I. INTRODUCTION. Artificial Intelligence (AI) has been studied for decades and is still one of the most elusive subjects in Computer Science. The Artificial Intelligence ranges from machines which are capable of thinking to search the procedures that necessarily require human senses to work upon. It founds its applications in nearly every way we use computers in society. The modern AI was developed by classical philosophers who attempted to elaborate the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s. The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on the subject. The main advancement in the AI has been made over the past fifty years particularly in search algorithms, machine learning algorithms, and integrating statistical analysis. However most of the breakthroughs in AI aren‟t noticeable to most people. A common theme in the field has been to overestimate the difficulty of foundational problems. In addition, there has always been a tendency to redefine what „intelligent‟ means after machines have mastered an area or problem. The problems analysed by A.I. can be classified according to their level. The first level includes problems of decision, learning, and perception, planning and reasoning. The second level includes tasks of classification, representation and search. II. SEARCH METHODS In the searching process, all the uninformed search methods share three common requirements : 1) a collection of facts based on a choice of representation providing the current state, and the goal state. 2) a set of operators which defines possible transformations of states and 3) a strategy which describes how transformations amongst states will be carried out by applying operators. Reasoning from a current state in search of a state which is closer to a goal state is known as forward reasoning. Reasoning backwards to a current state from a goal state is known as backward reasoning. The differentiation between: without information about the domain (Blind Search), and with information about of the domain (in this case, called Heuristic Search). can be exclusively made. A choice according to the kind of problem, between Extended Search and Deep Search together with other methods; some of these being derived. So, the ultimate or the final search is not the same as searching with the possibility of backward motion called as backtracking. State Space Search State space search is a process used in the computer science, including artificial intelligence (AI), in which consequent configurations or states of an instance are speculated, with the goal of finding a goal state with a desired property. Problems are often modelled as a state space, a set of states that a problem can be in. The set of states forms a graph where two states are connected if there is an operation that can be performed to transform the first state into the second. State space search often differs from traditional computer science search methods because the state space is implicit. The typical state space graph is much too large to generate and store in memory. Instead, nodes are generated as they are explored, and typically discarded thereafter. A solution to a combinatorial search instance may consist of the goal state itself, or of a path from some initial state to the goal state. Exhaustive search of a problem space (or search space) is often not feasible or practical due to the size of the problem space. In some instances it is however, necessary. More often, we are able to define a set of legal transformations of a state space (moves in