, PROBLEM -
SOLVING AGENTS
when the correct action to take is not obvious need
agent may to
•
,
plan ahead
creates sequence of actions that form a path to a goal state
Planning
•
such an agent = problem solving agent 9 process it undertakes search
• - =
•
Problem-solving agents use atomic representations ( states of the world
are considered as wholes) with no internal structure visible
to the
problem-solving algorithms
Agents that factored / structured rep
use
planning agents
• =
Considers simplest environments : episodic ,
single agent fully
•
,
observable , deterministic , static, discrete , known
•
Agent can follow 4- Phase
problem-solving process :
→ (1) Goal formation
Goals organize behaviour by limiting the objectives & hence
the actions to be considered
→ (2) Problem formulation
Agent devises a description of the states { actions necessary
to reach the
goal
→ (3) Search
Before action in the real world , the agent
taking any
simulates sequences of actions in its model searching until
,
it finds
a sequence of actions that reaches the goal
[sequence is called a solution]
→ (4) Execution
The the actions in the solution one at
agent can now execute
a time
SEARCH PROBLEMS
SOLVING AGENTS
when the correct action to take is not obvious need
agent may to
•
,
plan ahead
creates sequence of actions that form a path to a goal state
Planning
•
such an agent = problem solving agent 9 process it undertakes search
• - =
•
Problem-solving agents use atomic representations ( states of the world
are considered as wholes) with no internal structure visible
to the
problem-solving algorithms
Agents that factored / structured rep
use
planning agents
• =
Considers simplest environments : episodic ,
single agent fully
•
,
observable , deterministic , static, discrete , known
•
Agent can follow 4- Phase
problem-solving process :
→ (1) Goal formation
Goals organize behaviour by limiting the objectives & hence
the actions to be considered
→ (2) Problem formulation
Agent devises a description of the states { actions necessary
to reach the
goal
→ (3) Search
Before action in the real world , the agent
taking any
simulates sequences of actions in its model searching until
,
it finds
a sequence of actions that reaches the goal
[sequence is called a solution]
→ (4) Execution
The the actions in the solution one at
agent can now execute
a time
SEARCH PROBLEMS
