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Lecture 3: 18/4/1435
Searching for solutions.
Lecturer/ Kawther Abas
363CS – Artificial Intelligence
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Problem Solving Agents
• Problem solving agent– A kind of “goal based” agent– Finds sequences of actions that lead to desirable
states.
• The algorithms are uninformed– No extra information about the problem other than
the definition• No extra information• No heuristics (rules)
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Goal Based Agent
Goal Based Agent
Enviro
nm
ent
Percepts
Actions
What the world is like now
Sensors
ActuatorsWhat action I should do now
Goals
State
How the world evolves
What my actions doWhat it will be like
if I do action A
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Goal Based Agents
• Assumes the problem environment is:– Static
• The plan remains the same – Observable
• Agent knows the initial state– Discrete
• Agent can enumerate the choices– Deterministic
• Agent can plan a sequence of actions such that each will lead to an intermediate state
• The agent carries out its plans with its eyes closed– Certain of what’s going on– Open loop system
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Well Defined Problems and Solutions
• A problem– Initial state– Actions and Successor Function– Goal test– Path cost
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Searching For Solutions
• Initial State• Successor Function• Goal Test• Path Cost
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Searching For Solutions
• Having formulated some problems…how do we solve them?
• Search through a state space
• Use a search tree that is generated with an initial state and successor functions that define the state space
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Searching For Solutions
• A state is (a representation of) a physical configuration
• A node is a data structure constituting part of a search tree– Includes parent, children, depth, path cost
• States do not have children, depth, or path cost
• The EXPAND function creates new nodes, filling in the various fields and using the SUCCESSOR function of the problem to create the corresponding states
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Problem types• 1) Deterministic, fully observable • Agent knows exactly which state it will be in; solution is a sequence of
actions.• 2) Non-observable --- sensorless problem
– Agent may have no idea where it is (no sensors); it reasons in terms of belief states; solution is a sequence actions (effects of actions certain).
• 3) Nondeterministic and/or partially observable: contingency problem – Actions uncertain, percepts provide new information about current state
(adversarial problem if uncertainty comes from other agents).
• 4) Unknown state space and uncertain action effects: exploration problem-- Solution is a “strategy” to reach the goal (end explore environment).
•
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