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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
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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.