invitation to computer science 5 th edition chapter 15 artificial intelligence

Invitation to Computer Science 5th Edition

Chapter 15

Artificial Intelligence

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Objectives

In this chapter, you will learn about:

• A division of labor

• Knowledge representation

• Recognition tasks

• Reasoning tasks

• Robotics

Introduction

• Artificial intelligence (AI) – Explores techniques for incorporating aspects of

intelligence into computer systems

• Turing test – Allows a human to interrogate two entities, both

hidden from the interrogator

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Figure 15.1 The Turing Test

A Division of Labor

• Computational tasks– Adding a column of numbers– Sorting a list of numbers into numerical order– Searching for a given name in a list of names– Managing a payroll– Calculating trajectory adjustments for the space

shuttle

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A Division of Labor (continued)

• Recognition tasks– Recognizing your best friend– Understanding the spoken word– Finding the tennis ball in the grass in your backyard

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A Division of Labor (continued)

• Reasoning tasks– Planning what to wear today– Deciding on the strategic direction a company should

follow for the next five years– Running the triage center in a hospital emergency

room after an earthquake

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Figure 15.2 Human and Computer Capabilities

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Knowledge Representation

• Language schemes– Natural language– Formal language– Pictorial– Graphical

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Figure 15.3 A Semantic Net Representation

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Knowledge Representation (continued)

• Characteristics of knowledge representation scheme – Adequacy– Efficiency– Extendability

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Recognition Tasks

• Neuron– Cell capable of receiving stimuli, in the form of

electrochemical signals, from other neurons through its many dendrites

– Can send stimuli to other neurons through its single axon

• Artificial neural networks– Can be created by simulating individual neurons in

hardware and connecting them in a massively parallel network of simple devices

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Figure 15.4 A Neuron

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Figure 15.5 One Neuron with Three Inputs

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Figure 15.6 Neural Network Model

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Figure 15.7 A Simple Neural Network - OR Gate

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Figure 15.8 The Truth Table for XOR

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Figure 15.9 An Attempt at an XOR Network

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Recognition Tasks (continued)

• Neural network– Can learn from experience by modifying the weights

on its connections– Can be given an initial set of weights and thresholds

that is simply a first guess– Network is then presented with training data

• Back propagation algorithm– Eventually causes the network to settle into a stable

state where it can correctly respond to all inputs in the training set

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Reasoning Tasks

• Characteristic of human reasoning – Ability to draw on a large body of facts and past

experience to come to a conclusion

• Artificial intelligence specialists try to get computers to emulate this characteristic

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Intelligent Searching

• Decision tree for a search algorithm – Illustrates the possible next choices of items to

search if the current item is not the target

• Decision tree for sequential search is linear

• Classical search problem benefits from two simplifications– Search domain is a linear list– We seek a perfect match

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Figure 15.10 Decision Tree for Sequential Search

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Figure 15.11 Decision Tree for Binary Search

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Figure 15.12 A State-Space Graph with Exponential Growth

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Intelligent Searching (continued)

• Solution path – Takes us from the initial state to a winning

configuration, and the graph nodes along the way represent the intermediate configurations

• Brute force approach for a solution path – Traces all branches of the state-space graph

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Swarm Intelligence

• Swarm intelligence model – Uses simple agents that:

• Operate independently

• Can sense certain aspects of their environment

• Can change their environment

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Intelligent Agents

• Intelligent agent – Software technology designed to interact

collaboratively with a user somewhat in the mode of a personal assistant

• Examples– Personalized Web search engine– Web searcher that enables you to “vote” on each

article it sends you– Online catalog sales company that uses an agent to

monitor incoming orders and make suggestions

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Expert Systems

• Rule-based system – Attempts to mimic the human ability to engage

pertinent facts and string them together in a logical fashion to reach some conclusion

– Must contain these two components• A knowledge base

• An inference engine

• Modus ponens– Gives us a method for making new assertions

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• Forward chaining – Begins with assertions and tries to match those

assertions to the “if” clauses of rules

• Backward chaining – Begins with a proposed conclusion and tries to

match it with the “then” clauses of rules

Expert Systems (continued)

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Expert Systems (continued)

• Explanation facility– Allows the user to see the assertions and rules used

in arriving at a conclusion

• Knowledge engineering– Requires interaction with the human expert, much of

it in the domain environment

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Robotics

• Uses for robots in manufacturing, science, the military, and medicine– Assembling automobile parts– Packaging food and drugs– Placing and soldering wires in circuits– Bomb disposal– Welding– Radiation and chemical spill detection

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Robotics (continued)

• Two strategies characterize robotics research– Deliberative strategy: says that the robot must

have an internal representation of its environment– Reactive strategy: uses heuristic algorithms to

allow the robot to respond directly to stimuli from its environment

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Summary

• Artificial intelligence – Explores techniques that incorporate aspects of

intelligence into computer systems

• Categories of tasks– Computational, recognition, and reasoning

• Neural networks – Simulate individual neurons in hardware and connect

them in a massively parallel network

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Summary (continued)

• Swarm intelligence– Models the behavior of a colony of ants

• Intelligent agent interacts with a user

• Rule-based systems – Attempt to mimic the human ability to engage

pertinent facts and combine them in a logical way to reach some conclusion

• Robots can perform many useful tasks