11: Chapter Artificial Intelligence
Computer Science: An Overview Tenth Edition by J. Glenn Brookshear
Copyright 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley
Chapter 11: Artificial Intelligence
11.1 Intelligence and Machines 11.2 Perception 11.3 Reasoning 11.4 Additional Areas of Research 11.5 Artificial Neural Networks 11.6 Robotics 11.7 Considering the Consequences
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Intelligent Agents
Agent: A "device" that responds to stimuli from its environment
Sensors Actuators
Much of the research in artificial intelligence can be viewed in the context of building agents that behave intelligently
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Levels of Intelligent Behavior
Reflex: actions are predetermined responses to the input data More intelligent behavior requires knowledge of the environment and involves such activities as:
Goal seeking Learning
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Figure 11.1 The eight-puzzle in its solved configuration
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Figure 11.2 Our puzzle-solving machine
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Approaches to Research in Artificial Intelligence
Engineering track
Performance oriented
Theoretical track
Simulation oriented
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Turing Test
Test setup: Human interrogator communicates with test subject by typewriter. Test: Can the human interrogator distinguish whether the test subject is human or machine?
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Techniques for Understanding Images
Template matching Image processing
edge enhancement region finding smoothing
Image analysis
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Language Processing
Syntactic Analysis Semantic Analysis Contextual Analysis
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Figure 11.3 A semantic net
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Components of a Production Systems
1. Collection of states
Start (or initial) state Goal state (or states)
2. Collection of productions: rules or moves
Each production may have preconditions
3. Control system: decides which production to apply next
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Reasoning by Searching
State Graph: All states and productions Search Tree: A record of state transitions explored while searching for a goal state
Breadth-first search Depth-first search
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Figure 11.4 A small portion of the eight-puzzle's state graph
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Figure 11.5 Deductive reasoning in the context of a production system
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Figure 11.6 An unsolved eight-puzzle
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Figure 11.7 A sample search tree
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Figure 11.8 Productions stacked for later execution
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Heuristic Strategies
Heuristic: A "rule of thumb" for making decisions Requirements for good heuristics
Must be easier to compute than a complete solution Must provide a reasonable estimate of proximity to a goal
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Figure 11.9 An unsolved eight-puzzle
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Figure 11.10 An algorithm a for control system using heuristics
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Figure 11.11 The beginnings of our heuristic search
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Figure 11.12 The search tree after two passes
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Figure 11.13 The search tree after three passes
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Figure 11.14 The complete search tree formed by our heuristic system
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Handling Real-World Knowledge
Representation and storage Accessing relevant information
Meta-Reasoning Closed-World Assumption
Frame problem
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Learning
Imitation Supervised Training Reinforcement Evolutionary Techniques
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Artificial Neural Networks
Artificial Neuron
Each input is multiplied by a weighting factor. Output is 1 if sum of weighted inputs exceeds the threshold value; 0 otherwise.
Network is programmed by adjusting weights using feedback from examples.
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Figure 11.15 A neuron in a living biological system
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Figure 11.16 The activities within a processing unit
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Figure 11.17 Representation of a processing unit
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Figure 11.18 A neural network with two different programs
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Figure 11.19 An artificial neural network
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Figure 11.20 Training an artificial neural network
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Figure 11.20 Training an artificial neural network (continued)
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Figure 11.20 Training an artificial neural network (continued)
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