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International Journal of Conceptions on Computing and Information Technology Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808 The relationship between artificial intelligence and psychological theories Pratik and Rahul Abhishek Dept. of CSE & IT, Majhighariyani Institute of Technology and Science, Rayagada, Odisha, India. [email protected] and [email protected] Abstract— Psychology is one of the parent elements of artificial intelligence or we can also say that it is the main source for artificial intelligence. In this paper we are discussing about the theories of psychology used in AI. Since psychology is the study of human brain and its nature and AI is the branch which deals with the intelligence in machine, so for understanding the intelligence of a machine we have to compare with human intelligence because AI means the intelligence shown by a machine like a human being. Keywords- Artificial Intelligence (AI), Psychology, Perception, Learning, Neural network, Cognitive Science, Human Computing Interaction (HCI). I. INTRODUCTION Artificial Intelligence the word coined by John McCarthy three decade ago now becomes a revolutionary word in the field of computer science and information technology. What actually we mean by AI? According to M.L. Minsky, "Artificial intelligence is the science of making machines do things that would require intelligence if done by men." Artificial Intelligence (AI) is a perfect example of how sometimes science moves more slowly than we would have predicted. Another representative definition of AI is pivoted around the comparison of intelligence of computing machine with human beings. None of these definitions or the like has been accepted, perhaps because of their references to the word “intelligence, which at present is an abstract and immeasurable quantity. A better definition of artificial intelligence, therefore, calls for formalization of the term “intelligence”. Psychologist and Cognitive theorists are of the opinion that intelligence helps in identifying the right piece of knowledge at the appropriate instances of decision making. The phrase “artificial intelligence” thus defined by C Bane as “the simulation of human intelligence on a machine, so make the machine efficient to identify and use the right place of knowledge at a given step of solving a problem II. PERCEPTION IN AI The definition of AI is based on the nature of the problems it tackles, namely those for which humans currently outperform computers. Perception involves interpreting sights, sounds, Figure 1. Parent element of Artificial Intelligence smells and touch. Action includes the ability to negative through the world and manipulate objects. In perception the environment is scanned by means of various sensory organs, real or artificial, and the scene is decomposed into separate objects in various spatial relationships. Analysis is complicated by the fact that an object may appear different depending on the angle from which it is viewed, the direction and intensity of illumination in the scene, and how much the object contrasts with the surrounding field. The difficulty of implementing analogues of human perception has been underestimated by AI researchers. At present, artificial perception is sufficiently well advanced to enable optical sensors to identify individuals, autonomous vehicles to drive at moderate speeds on the open road, and robots to roam through buildings collecting empty soda cans. One of the earliest systems to integrate perception and action was FREDDY, a stationary robot with a moving television eye and a pincer hand, constructed at the University of Edinburgh, 57 | 7 1 International Journal of Conceptions on Computing and Information Technology Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808 Scotland, during the period 1966–73 under the direction of Donald Michie. FREDDY was able to recognize a variety of objects and could be instructed to assemble simple artifacts, such as a toy car, from a random heap of components. There are basically two approaches for perception i. Feature Extraction - It detects some small number of features in sensory input and passes them to their agent programand this agent program will combine features with other information ii. Model Based - Sensory stimulus is used to reconstruct a model of the world it start with a function that maps from a state of the world to a stimulus. In reality, both feature extraction and model-based approaches are needed. The recent extraordinary growth of artificial intelligence and its application mainly in the development of expert systems, practical implementations of natural languageunderstanding systems, significant advances in computer vision and speech understanding. According to psychology Learning can be defined as the process leading to relatively permanent behavioural change or potential behavioural change and this definition also fits on machines. The experimental programs developed in the course of machine learning research, current AI systems have very less learning ability or we can say none at all. All of their knowledge must be programmed into them. When they contain an error, they cannot correct it on their own; they will repeat it again and again. They can neither improve gradually with experience nor learn domain knowledge by experimentation. They cannot automatically generate their algorithm, formulate new abstractions, or develop new solutions by drawing analogies to old ones, or through discovery and when we look at human intelligence we see that among its most striking aspects is the ability to acquire new knowledge, and to improve with practice. The ability to learn from error is considered fundamental to the individual. So learning ability is so intimately entwined with intelligence behaviour research in AI gives us new insights and powerful tools to study it, the new central goals for research in artificial intelligence should be understand the nature of learning and implementing learning capabilities in machines. According to psychology a human learn by his experiences in daily life. IV. HOW A HUMAN BRAIN LEARNS? The learning process in human is done through nervous system this nervous system consists of neuron. These neurons collect signals with the help of dendrites; these tiny protrusions receive information from other neurons and transmit electrical stimulation to the soma. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. When neuron receive sufficient amount of input then it sends an electrical signal. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes. Figure 2. Road map of computer vision III. LEARNING IN AI There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and procedures is known as rote learning. 58 | 7 1 Figure 3. Structure of neuron International Journal of Conceptions on Computing and Information Technology Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808 V. MACHINE LEARNING THOUGH ARTIFICIAL NEURON An artificial neuron is a computational model inspired in the natural neurons. Natural neurons receive signals through synapses located on the dendrites or membrane of the neuron. When the signals received are strong enough (surpass a certain threshold), the neuron is activated and emits a signal though the axon. This signal might be sent to another synapse, and might activate other neurons. The complexity of real neurons is highly abstracted when modeling artificial neurons. These basically consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron. Another function (which may be the identity) computes the output of the artificial neuron (sometimes in dependence of a certain threshold). ANNs combine artificial neurons in order to process information. The information processing model of intelligence itself has been a stimulant to psychology. Cognitive psychology and AI share many metaphors. Clowes discusses the relationship between artificial intelligence and psychology by considering as an examine problem which is one of the central problem of AI: computer vision. The brain is an information processing device. AI supports also this idea. The fig 5 illustrates the information processing model of the brain. In the human brain there are roughly 20 billion neurons (the number depends on various factors, including age and gender). Each neuron will be connected through synapses to roughly 10,000 other neurons. There's no way we can mimic 20 billion neurons with 10,000 connections each, but there are several interesting things we can do with much less firepower. Way back in 1957, Frank Rosenblatt modeled a single neuron with something he called a 'perceptron', and used it to investigate pattern recognition. Unfortunately, the perceptron was unable to recognize even simple functions like XOR (proved formally by Marvin Minsky and Seymour Papert in 1969) and so it was abandoned in favor of something called multilayer feed forward networks. Figure 5. Information Processing Model of the Human Brain a. Stimulus has to be interpreted to representation b. Representation is manipulated via cognitive processes, and builds new inside representation c. Process may end in an action. According to Craik (scientists dealing with knowledge based agents, and died in an unfortunate accident) the organism contains a possible small model of the outer world, and the possible actions, is also able to try different alternatives, and decide by the best, react before an expected future happens, or to analyze the consequences of the past, and react the most competent and safe way on a situation. (Craik, 1943). Figure 4. Artificial neural network VI. COGNITIVE SCIENCE AND ARTIFICAL INTELLIGENCE How can we connect artificial intelligence with cognitive psychology? What kind of models and approaches were developed in these scientific fields? AI is more and more evolving into a science of intelligence, and a report of work on computer semantics make clear that there is a great deal of emphasis on how the mind might, if not does, go about doing some of the intelligence tasks involved in resolving meaning. We may come in a contact with Human Computing Interaction (HCI) every day, because this field includes the everyday use of computer, the user interfaces and expert programs which may use cognitive psychology in order to manipulate or help people. It is important in the HCI to understand the goals, intention of the user the problem solving ability (with psychology), to understand the interaction (sociology), and to understand the physical ability of the users (ergonomic), to develop useful interface (graphical design), and to develop a system (computer science). We can find HCI application in virtual reality and virtual environment. Virtual Reality is a new way of the human machine communication, which enables an interaction connecting to human senses. 59 | 7 1 International Journal of Conceptions on Computing and Information Technology Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808 Virtual Environment is an important part of Virtual Reality, because more and more people connect to them. In these artificial environments (for example: games), human like attributes are relevant. Technologies of Artificial Intelligence provide a basis for the dream of the virtual reality. they know far more about Artificial Intelligence than they actually do. VII. CONCLUSION The paper provide sort but broad summery about the psychological theories which is related to artificial intelligence. The area of AI and psychology is dynamic it is changing day by day. The field of AI has a reputation for making huge promises and then failing to deliver on them. The role of perception and action in current AI systems has been analyzed. Learning techniques are discussed with comparison of human learning process and artificial learning process. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think [2] REFERENCES [1] [3] [4] [5] [6] [7] [8] [9] 60 | 7 1 “Basic Psychology” by Ross Stanger, Charles M. Solly, (1970), Tata McGram Hill Publishing Co. Ltd. Artificial Intelligence Chapter 24, Michael Scherger, Department of Computer Science, Kent State University. Artificial Intelligence and Natural Perception, Prof dr. Lambert Schomaker, AI institute, Groningen University, The Netherlands. Mind Design II philosophy, psychology, artificial intelligence, by John haugeland, the MIT press, Cambridge, Massachusetts, London, England. Samanya Manovigyan by Sinha and Prasad. “Machine Learning A Multistrategy Approch” Volume 4, by Ryszard Michalski, Gheorghe Tecuci.) Handbook of Child Psychology, Vol. 2: Cognition, Perception, and Language, 6th Edition (Volume 2 ), by William Damon, Richard M. Lerner, Deanna Kuhn , Robert S. Siegler. “Abnormal psychology”, by Ann Kring, Sheri Johnson, Gerald C. Davison, John M. Neale. “The Human Mind: how we think and learn”, by Thomas H. Metos