CptS
Artificial Intelligence
Search
, Search
• Search permeates all of AI
• What choices are we searching through?
– Problem solving
Action combinations (move 1, then move 3, then move 2...)
– Natural language
Ways to map words to parts of speech
– Computer vision
Ways to map features to object model
– Machine learning
Possible concepts that fit examples seen so far
– Motion planning
Sequence of moves to reach goal destination
• An intelligent agent is trying to find a set or sequence of actions to
achieve a goal
• This is a goal-based agent
,Problem-solving Agent
SimpleProblemSolvingAgent(percept)
state = UpdateState(state, percept)
if sequence is empty then
goal = FormulateGoal(state)
problem = FormulateProblem(state, g)
sequence = Search(problem)
action = First(sequence)
sequence = Rest(sequence)
Return action
, Assumptions
• Static or dynamic?
Environment is static
Artificial Intelligence
Search
, Search
• Search permeates all of AI
• What choices are we searching through?
– Problem solving
Action combinations (move 1, then move 3, then move 2...)
– Natural language
Ways to map words to parts of speech
– Computer vision
Ways to map features to object model
– Machine learning
Possible concepts that fit examples seen so far
– Motion planning
Sequence of moves to reach goal destination
• An intelligent agent is trying to find a set or sequence of actions to
achieve a goal
• This is a goal-based agent
,Problem-solving Agent
SimpleProblemSolvingAgent(percept)
state = UpdateState(state, percept)
if sequence is empty then
goal = FormulateGoal(state)
problem = FormulateProblem(state, g)
sequence = Search(problem)
action = First(sequence)
sequence = Rest(sequence)
Return action
, Assumptions
• Static or dynamic?
Environment is static
