Artificial IntelligenceFocus and Application

Dr. S Krishnakumar, DRDO, Chennai

Course Topics Session – 1……… 9-30 to 10-30

Artificial Intelligence – Overview What is AI What is Machine Learning

Session -2………. 10-45 to 12-30 Application of AI

Sensing Networking Databases Prediction and Data Mining Decision Making Robotics Privacy Issues

Overview

What is Artificial Intelligence

Artificial Intelligence is the Science and Engineering that is concerned with the theory and practice of developing systems that exhibit the characteristics we associate with intelligence in human behavior: perception, natural language processing, reasoning, planning and problem solving, learning and adaptation, etc.

What is Artificial Intelligence

Central goals of Artificial Intelligence

Understand the principles that make intelligence possible(in humans, animals, and artificial agents)

Developing intelligent machines or agents(no matter whether they operate as humans or not)

Formalizing knowledge and mechanizing reasoningin all areas of human endeavor

Making the working with computers as easy as working with people

Developing human-machine systems that exploit the complementariness of human and automated reasoning

What is an intelligent agent

IntelligentAgent

user/environment

output/

sensors

effectors

input/

An intelligent agent is a system that: • perceives its environment (which may be the physical world, a user via a graphical user interface, a collection of other agents, the Internet, or other complex environment); • reasons to interpret perceptions, draw inferences, solve problems, and determine actions; and• acts upon that environment to realize a set of goals or tasks for which it was designed.

Characteristic features of intelligent agents

Knowledge representation and reasoning

Transparency and explanations

Ability to communicate

Use of huge amounts of knowledge

Exploration of huge search spaces

Use of heuristics

Reasoning with incomplete or conflicting data

Ability to learn and adapt

Overview

What is Machine Learning

Machine Learning is the domain of Artificial Intelligence which is concerned with building adaptive computer systems that are able to improve their competence and/or efficiency through learning from input data or from their own problem solving experience.

What is Machine Learning

The architecture of a learning agent

Ontology

Rules/Cases/Methods

Problem SolvingEngine

Learning Agent

User/Environment Output/

Sensors

Effectors

Input/

Knowledge Base

LearningEngine

Implements learning

methods for extending and refining

the knowledge base to improve agent’s

competence and/or

efficiency in problem solving.

Implements a general problem solving method that uses the knowledge from the knowledge base to interpret the

input and provide an appropriate output.

Data structures that represent the objects from the application domain, general laws governing them, actions that can be performed with them, etc.

What is Learning?

Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more effectively the next time (Simon, 1983).

Learning is making useful changes in our minds (Minsky, 1985).

Learning is constructing or modifying representations of what is being experienced (Michalski, 1986).

A computer program learns if it improves its performance at some task through experience (Mitchell, 1997).

So what is Learning?

(1) acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality);

(2) discover new knowledge and theories (by creating hypotheses that explain some data or phenomena);

(3) acquire skills (by gradually improving their motor or cognitive skills through repeated practice, sometimes involving little or no conscious thought).

Learning results in changes in the agent (or mind) that improve its competence and/or efficiency.

Learning is a very general term denoting the way in which people and computers:

Two complementary dimensions for learning

A system is improving its competence if it learns to solve a broader class of problems, and to make fewer mistakes in problem solving.

A system is improving its efficiency, if it learns to solve the problems from its area of competence faster or by using fewer resources.

Competence

Efficiency

Main directions of research in Machine Learning

Discovery of general principles, methods,and algorithms of learning

Automation of the constructionof knowledge-based systems

Modeling human learning mechanisms

Learning strategies

• Rote learning• Learning from instruction• Learning from examples• Explanation-based learning• Conceptual clustering• Quantitative discovery• Abductive learning• Learning by analogy

• Instance-based learning• Reinforcement learning• Neural networks• Genetic algorithms and evolutionary computation• Reinforcement learning• Bayesian learning• Multistrategy learning

A Learning Strategy is a basic form of learning characterized by the employment of a certain type of inference (like deduction, induction or analogy) and a certain type of computational or representational mechanism (like rules, trees, neural networks, etc.).

Session-2 Application of AI

Sensing Networking Databases Prediction and Data Mining Decision Making Robotics

Intelligent Environments Environments that use technology to assist

inhabitants by automating task components Aimed at improving inhabitants’ experience and

task performance NOT: large number of electronic gadgets

Objectives ofIntelligent Environments

Improve Inhabitant experience: Optimize inhabitant productivity Minimize operating costs Improve comfort Simplify use of technologies Ensure security Enhance accessibility

Requirements forIntelligent Environments

Acquire and apply knowledge about tasks that occur in the environment

Automate task components that improve efficiency of inhabitant tasks

Provide unobtrusive human-machine interfaces Adapt to changes in the environment and of the inhabitants Ensure privacy of the inhabitants

Examples of Intelligent Environments Intelligent Workspaces

Automatic note taking Simplified information sharing Optimized climate controls Automated supply ordering

Examples of Intelligent Environments Intelligent Vehicles

Location-aware navigation systems Task-specific navigation Traffic-awareness

Examples of Intelligent Environments Smart Homes

Optimized climate and light controls Item tracking and automated ordering for food

and general use items Automated alarm schedules to match

inhabitants’ preferences Control of media systems

Tech Aware Home Perceive and assist occupants Aging in Place (crisis support) Ubiquitous sensing

Scene understanding, object recognition Multi-camera, multi-person tracking Context-based activity

Smart floor

Intelligent Room Support natural interaction with room

Speech-based information access Gesture recognition Movement tracking Context-aware automation

Interactive Workspaces Large wall and tabletop interactive

displays Scientific visualization Mobile computing devices Computer-supported cooperative work Distributed system architectures

Adaptive House Infer patterns and predict actions Machine learning for automation HVAC, water heater, lighting control Goals:

Reduce occupant manual control Improve energy efficiency

Smart Home Learning of inhabitant patterns Learn optimal automation strategies Goals

Maximize comfort and productivity Minimize cost

Ensure security

Smart Home Inhabitant Prediction Smart entertainment control Smart kitchen recipe services Household staff modeling

General Electric Smart Home Appliance control interfaces Climate control Energy management devices Lighting control Security systems Consumer Electronics Bus (CEBus)

Microsoft Easy Living Camera-based person detection and tracking Geometric world modeling for context Multimodal sensing Biometric authentication Distributed systems Ubiquitous computing

Vision of the Future Less obtrusive technology Technology devices

Interactive wallpaper Control wands Intelligent garbage can

Connected Family Remote monitoring of the home Entry authentication Integrated, pervasive communications Centralized data management

Challenges inIntelligent Environments

Home design and sensor layout Communication and pervasive computing Natural interfaces Management of available data Capture and interpretation of tasks Decision making for automation Robotic control Large-scale integration Inhabitant privacy

Sensors How many and what type?

How to interpret sensor data?

How to interface with sensors?

Are sensors active or passive?

Communications What medium and protocol? How to handle bandwidth limitations? What structure does the communication

infrastructure have?

Data Management How to store all the data? What data is stored? How is data distributed to the pervasive

computing infrastructure?

Prediction & Decision Making How to extract and represent

inhabitants’ task patterns? What patterns should be maintained? How to determine the actions to

automate? To what level should tasks be

automated?

Automation How are the tasks automated? How are actuators controlled? How is safety ensured?

End

End....Less.......