Artificial Intelligence (AI) - History And Foundation, AI Techniques, Problem Solving With AI Models, Data Acquisition And Learning Aspects In AI.

1. AI TECHNIQUES
• Structured problems
• Unstructured problems
• Linear problems
• Non linear problems

2. Problem solving with AI
• Some of the very commonly faced problems
are structured and unstructured during day to
day
• Some of the well structured problems are
1. Solving quadratic equation to find out value of x
2. Calculating path of trajectory when a missile is
fired

3. Problem solving with AI
• Some of ill structured problems are given
below
1. Predicting how to dispose wet waste safely
2. Identifying the security threat in social gathering
3. Analysis of theoretical prepositions and
adequacy of the same in a particular scenario

4. AI Models
Knowledge based model
building
AI application building
Discover relationship Mapping
model
Discover
complexity

5. AI Models
• Semiotic Models
• Based on sign process and communication
• Assignment and mapping process depends on the
use of codes based on individual sound
• Statistical models
• Representation and formalisation of relationships
through statistical techniques
• Most of the AI problems are pattern matching
problems

6. AI Models
• Historical data is used for decision making
based on probabilistic approaches i.e
collection of probability density function and
distribution function

7. DATA ACQUISITION AND LEARNING
ASPECTS IN AI
• knowledge discovery and learning aspects in AI
• Data mining is nothing but extraction of meaningful
information that is previously unknown
• Main concern of data mining is data analysis and use of
suitable techniques to identify and recognise the
patterns to give good prediction

8. DATA ACQUISITION AND LEARNING
ASPECTS IN AI
• The mining process includes data cleaning, pre
processing, identifying, interpreting the patterns,
understanding the application and generating the
target data
• It acts as a tool and holds core part in business
intelligence
• Machine learning is a field concerned with
study of algorithms that will improve its
performance with experience, main focus is
on improving performance of agent.

9. DATA ACQUISITION AND LEARNING
ASPECTS IN AI
• Computational learning theory(COLT)
• By defining formal mathematical models we are
analysing the efficiency complexity in terms of
computation prediction and feasibility of algorithms
• Computation learning theory finds its importance in
machine learning pattern recognition and statistics.
• There are two frameworks for analysing the patterns
1. probably approximately correct
2. mistake bound

10. DATA ACQUISITION AND LEARNING
ASPECTS IN AI
• Neural and evolutionary computation
• this method enabled to speed up the mining of
data
• Evolutionary computing is related to the study of
biological properties like genetic algorithms.
• Telecom domain to financial decision making, with
optimization as base criteria.
• In case of neural computing neural behaviour of
human brain is stimulated to enable machine to
learn

11. DATA ACQUISITION AND LEARNING
ASPECTS IN AI
• ANN is configured for specific application like pattern
recognition
• Intelligent agent and multi-agent systems
• These type of intelligent system allows timely
decision making in complex scenarios.
• An agent is simple software program that
assists user

12. DATA ACQUISITION AND LEARNING
ASPECTS IN AI
• An intelligent agent is flexible in terms of its action to get
the desired output.
• It is goal directed, reacts with environment and acts
accordingly.
• Complex tasks and decision-making demand combination
of more than one percept of different intelligent systems
which can be done by multi-agent system
• Here every agents capacity and its computation
efficiency is exploited so that overall performance is
improved

13. DATA ACQUISITION AND LEARNING
ASPECTS IN AI
• Multi perspective integrated intelligence
• Exploiting and utilising information from different
perspectives to build up an intelligent system giving
accurate results builds this frame work.
• Taking feedback