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ARTIFICIAL INTELLIGENCE DEFINITION 1. AI is the study of mental faculty as through computational methods/modules. By Charniac (Systems that think rationally) law of thought approach Greek philosopher Aristotle, his syllogism makes the study initiated the field is called logic. 2. AI is the study of teaching computer do things at present, people do better. By rich & Knight (Systems that act like human) 3. AI is the ability to acquire, understand and apply knowledge/the ability to exercise knowledge with thought and reason. (Systems that think like human) Cognitive science :- Actual working of human mind. Two ways which are a. through introspection (trying to catch our own thought) b. psychological experiments. 4. CI is the study of the design of intelligent agent. Agent is just something that acts. A rational agent is one that acts so as to achieve the best outcome. (Systems that act rationally) Agent perceiving it’s environment through sensor and acting upon that environment through actuators. Like human having eye, ear act as sensor and leg, hand as actuator. Many researchers consider ROBOTICS as a separate interdisciplinary field which combines concept and techniques from AI, Electrical, Mechanical, Optical Engineering. AI TECHNIQUES 1. The problem domain is astronomical in size. 2. The adaptivities of the physical system. 3. The updation of database without human interference. 4. Generate and test. INTELLIGENCE As the ability to acquire, understand and apply knowledge or the ability to exercise thought and reason. Of course intelligence is more than this. It embodies all of the knowledge and feats, both conscious and unconscious, which we have acquired through study and experience, highly refined sight and sound perception, thought, imagination, the ability to converse, read, write, memorize and recall facts, express and feel emotions and much more. KNOWLEDGE State of knowing. In biological organism, knowledge is likely stored as complex structure of interconnected neurons (a nerve cell). The structures correspond to symbolic representation of the knowledge possessed by the organism, the facts, rules and so on. The average human brain weights about 3.3 pounds and contains an estimated number of 1012 neurons. The neurons and their connection capabilities provide about 1014 bits of potential storage capacity. (Segan 1977). Knowledge may be declarative (passive knowledge as statements of fact about the world) or procedural (steps used to solve an algebraic equation are expressed). Heuristic knowledge:- a special type of knowledge used by humans to solve complex problems. Heuristics are the knowledge used to make good judgement or the strategies, tricks or “rules of thumb” used to simplify the solution of problems. Heuristics are usually acquired with much experience. For example in locating a fault in a TV set. Epistemology : is the study of the nature of knowledge. Metaknowledge : is knowledge about knowledge i.e knowledge about what we know. Finally our overall picture of knowledge cannot be complete without also knowing the meaning of closely related concepts such as understanding, learning, thinking, remembering and reasoning. These concepts all depend on the use of knowledge. KNOWLEDGE BASED SYSTEM Japanese recognized the potential offered with these knowledge system, for development of super-computer. In the book “Fifth Generation” Feigenbaum and Mccorduck (1983) argue that the time is right for the exploitation of AI and that the leaders in this field will become the leaders in world trade. The most important one is expert system which is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally require human expertise. Different application areas are 1. 2. 3. 4. 5. medical diagnoses (MYCIN to diagnose infectious blood diseases ) diagnoses of complex electronic and electromechanical system. planning experiments in biology, chemistry and molecular genetics. forecasting crop damage. VLSI design and many more. Some of the basic research priorities related to knowledge base system 1. 2. 3. 4. Knowledge Representation. Knowledge Organization. Knowledge Manipulation. Knowledge Acquisition. REPRESENTATION OF KNOWLEDGE Knowledge consists of facts, concepts, rules and so forth. It can be represented in different forms as mental images in one’s thoughts as spoken or written words in some languages as graphical or other pictures and as a character strings or collection of magnetic spots stored in a computer. Any choice of representation will depend on the type of problem to be solved and the inference methods available. I/O UNITS ------ INFERENCE CONTROL UNIT ------ KNOWLEDGE BASE (Components of knowledge base system) The knowledge is stored in a knowledge base separate from the control and inference components. This makes it possible to add new knowledge or refine existing knowledge. To build a system to solve a particular problem we need 1. define the problem precisely. 2. analyze the problem 3. isolate and represent the task knowledge to solve the problem. 4. choose the best solving technique and apply it. There are several knowledge representation schemes. The most important of these is 1. FOPL :- First Order Predicate Logic a. no fixed syntax and semantic b. only facts and rule c. facts to be written in predicative form. 2. Statistical Reasoning (Baye’s Theorem) rule based system 3. Weak slot and filler structure (semantic net) Information is represented as a set of nodes connected to each other by a set of labeled arcs which represent relationship among the nodes. 4. Frame is a collection of attributes (slots) and associated values that describe some entity in the world. 5. Strong slot : conceptual dependency (CD) is a theory of how to represent the kind of knowledge about events that is usually contained in natural language sentences. 6. Script is a structure that describes a stereotyped sequence of events in a particular context. It consists of a set of slots. 7. CYC is a very large knowledge base project aimed at capturing human common sense knowledge. KNOWLEDGE ORGANISATION Knowledge can be organized in memory for easy access by a method known as indexing. It amounts to grouping the knowledge in a way that keywords can be used to access the group. The keywords “point” to the knowledge group. KNOWLEDGE MANIPULATION Decisions and actions in knowledge-based systems come from manipulation of the knowledge in specified ways. Typically some form of input will initiate a search for a goal or decision. This requires that known facts in the knowledge-base be located , compared (matched) and possibly altered in some way. This process may set up other subgoals and require further inputs and so on until a final solution is found. This requires a form of inference or deduction using the knowledge and inferring rule. KNOWLEDGE ACQUISITION Knowledge comes from various sources such as experts, text books, report, technical articles and the like. To be useful the knowledge must be accurate, presented at the right level for encoding, complete in the sense that all essential facts and rules are included free of inconsistencies and so on.