AI3391 ARTIFICIAL INTELLIGENCE Department of Artificial intelligence and data science under anna university syllabus

1. AI3391 ARTIFICAL INTELLIGENCE
(II YEAR (III Sem))
Department of Artificial Intelligence and Data Science
Session 13
by
Asst.Prof.M.Gokilavani
NIET
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2. TEXTBOOK:
• Artificial Intelligence A modern Approach, Third Edition, Stuart Russell
and Peter Norvig, Pearson Education.
REFERENCES:
• Artificial Intelligence, 3rd Edn, E. Rich and K.Knight (TMH).
• Artificial Intelligence, 3rd Edn, Patrick Henny Winston, Pearson
Education.
• Artificial Intelligence, Shivani Goel, Pearson Education.
• Artificial Intelligence and Expert Systems- Patterson, Pearson Education.
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3. Topics covered in session 13
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Unit II: Problem Solving
• Heuristic search Strategies
• Heuristic function
• Local search and optimization problems
• Local search in continuous space
• Search with non deterministic actions
• Search in partial observation environments
• Online search agents and unknown environment

4. Introduction
• In an environment, the agent can calculate exactly which state results from any
sequence of actions and always knows which state it is in.
Searching with non-deterministic Actions
Searching with partial observations
• When the environment is nondeterministic, percepts tell the agent which of the
possible outcomes of its actions has actually occurred.
• In a partially observable environment, every percept helps narrow down the set of
possible states the agent might be in, thus making it easier for the agent to achieve
its goals.
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5. Example: Vacuum world, v2.0
• In the erratic vacuum world, the Suck action works as follows:
• When applied to a dirty square the action cleans the square and
sometimes cleans up dirt in an adjacent square, too.
• When applied to a clean square the action sometimes deposits
dirt on the carpet.
• Solutions for nondeterministic problems can contain nested if–then–
else statements; this means that they are trees rather than sequences.
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6. Example: Vacuum world, v2.0
The eight possible states of the
vacuum world; states 7 and 8 are
goal states.
• Suck(p1, dirty)= (p1,clean) and
sometimes (p2, clean)
• Suck(p1, clean)= sometimes
(p1,dirty)
Solution : contingency plan
• [Suck, if State = 5 then [Right,
Suck] else [ ]] .
• nested if–then–else statements
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7. • Non-deterministic action= there may be several possible outcomes
• Search space is an AND-OR tree
• Alternating OR and AND layers
• Find solution= search this tree using same methods.
• Solution in a non-deterministic search space
• Not simple action sequence
• Solution= subtree within search tree with:
• Goal node at each leaf (plan covers all contingencies)
• One action at each OR node
• A branch at AND nodes, representing all possible outcomes
• Execution of a solution = essentially
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AND–OR search trees

8. AND–OR search trees
• The first two levels of the search tree
for the erratic vacuum world.
• State nodes are OR nodes where some
action must be chosen.
• At the AND nodes, shown as circles,
every outcome must be handled, as
indicated by the arc linking the
outgoing branches.
• The solution found is shown in bold
lines.
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9. Non-deterministic search trees
• Start state = 1
• One solution:
1. Suck,
2. if(state=5) then [right, suck] ]
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10. Non-determinism: Actions that fail (Try, try again)
• Action failure is often a non-
deterministic outcome
• Creates a cycle in the search tree.
• If no successful solution (plan)
without a cycle:
• May return a solution that contains a
cycle
• Represents retrying the action
• Infinite loop in plan execution?
• Depends on environment
• Action guaranteed to succeed
eventually?
• In practice: can limit loops
• Plan no longer complete (could fail)
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11. Non-determinism: Actions that fail (Try, try again)
• Part of the search graph for the slippery vacuum world, where we
have shown (some) cycles explicitly.
• All solutions for this problem are cyclic plans because there is no way
to move reliably.
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12. Searching with partial observations
• In a partially observable environment, every percept helps narrow
down the set of possible states the agent might be in, thus making it
easier for the agent to achieve its goals.
• The key concept required for solving partially observable problems is
the belief state.
• belief state -representing the agent’s current belief about the
possible physical states.
• Searching with no observations
• Searching with observations
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13. Conformant (sensorless) search: Example space
• Belief state space for the super simple vacuum world
• Observations:
– Only 12 reachable states. Versus 2^8= 256 possible belief
states
– State space still gets huge very fast! à seldom feasible in
practice
– We need sensors! à Reduce state space greatly!
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15. Searching with no observations
• (a) Predicting the next belief state for the sensorless vacuum world
with a deterministic action, Right.
• (b) Prediction for the same belief state and action in the slippery
version of the sensorless vacuum world.
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16. Searching with observations
• (a) In the deterministic world, Right is
applied in the initial belief state,
resulting in a new belief state with
two possible physical states; [B,
Dirty] and [B, Clean].
• (b) In the slippery world, Right is
applied in the initial belief state,
giving a new belief state with four
physical states; [A, Dirty], [B, Dirty],
and [B, Clean].
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17. Topics to be covered in next session 14
• online search agents and unknown environments.
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Thank you!!!