IDSV Chap. 11 - Artificial Intelligence
Heuristics
Mental shortcuts or "rules of thumb" that often lead to a solution (but not always). rules of thumb for making descisions
Current robots are successful on specific tasks:
Mobility (swim, fly, hop, etc) driving cars behave like pet dogs guide weapons to their targets
image analysis
understanding what the image characteristics represent in the real world
Contextual analysis
The context is brought into understanding process: "The bat fell to the ground"
intelligent agent
autonomous goal-directed entity which observes using SENSORS and acts upon an environment using ACTUATORS
How do artificial neuron networks learn?
From examples by adjusting their weight
Turing test
Human interrigator communicates with test subject by type writer, can the human interrogator distinguish wether the test subject is human or a machine?
Learning strategies
Learning by IMITATION SUPERVISED learning learning by REINFORCEMENT
Artificial Neuron
each weight is multiplied by a wrighting factor if sum of weighted inputs exeed theshhold then output is 1 else output is 0
Image processing steps
edge enhancement region finding
Semantic analysis
identifies the information content in a sentence: "Mary gave John a birthday card"/ "John got a birthday card from Mary" "Do you know what time it is?"
Syntactic analysis
indentifies the grammatical role of each word (parsing)
Requirements for good heuristics
it should constitute a reasonable estimate of proximity to a goal it should be easy to compute
Strong AI
machines can be programmed to possess intelligence and conciousness
Weak AI
machines can be programmes to exhibit intelligent behavior
Production systems
model of performance based on if-then {conditions-action} commands
learning by reinforcement
the agent is given a general rule to judge for itself when it has succeeded or failed
learning by imitation
the computer records the steps performed by a person
Robotics
the study of physical agents that behave intelligently
image processing definition
to change an image in order to emphasize the most important parts (identifying characteristics of an image)
Production system steps
1. collection of states -start/initial state -goal state 2. collection of productions (rules/moves) -from one state to another -might have preconditions 3. control system - decides what production to apply next
supervised learning
a person identifies the correct response for a number of examples and the agent generalizes from those examples
Semantic net
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search tree example
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state graph
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Language processing analyses:
Syntactic analysis Semantic analysis Contextual analysis
