UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdf

SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Solving Problems by Searching
• Reflex agent is simple(Cannot Operate in Well Environment, its Action
–Based on Current Situation and ignoring the History of Perception)
e.g Car Front Brakes
• base their actions on
• a direct mapping from states to actions
• but cannot work well in environments
• which this mapping would be too large to store
• and would take too long to learn
• Hence, goal-based agent is used

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Problem-solving agent
• Problem-solving agent
• A kind of goal-based agent – Choose their actions in order to achieve Goals.
• This Allows the Agent to a way of Choosing among Multiple Possibilities,
selecting the one which reaches a Goal states.
• It Requires Searching and Planning Techniques. E.g. GPS – Finding a path to
certain Destination.
• It solves problem by
• finding sequences of actions that lead to desirable states (goals)
• To solve a problem,
• the first step is the goal formulation, based on the current situation

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Problem Solving Agents :
• Intelligent Agent are Supposed to “ To Maximize their
Performance Measure ”
• Achievement : It can Adopt a Goal and Aim at Satisfying.

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Goal formulation
• The goal is formulated
• as a set of world states, in which the goal is satisfied
• Reaching from initial state 🡪 goal state
• Actions are required
• Actions are the operators
• causing transitions between world states
• Actions should be abstract enough at a certain degree, instead of
very detailed
• E.g., turn left VS turn left 30 degree, etc.

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Problem formulation
Well-defined problems and solutions
A problem is defined by 5 components:
• Initial state
• Actions
• Transition model or
(Successor functions)
• Goal Test.
• Path Cost.

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• A problem is defined by 4 components:
• The initial state
• that the agent starts in
• The set of possible actions
• Transition model: description of what each action does.
(successor functions): refer to any state reachable from given
state by a single action
• Initial state, actions and Transition model define the state
space
• the set of all states reachable from the initial state by any
sequence of actions.
• A path in the state space:
• any sequence of states connected by a sequence of actions.

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• The goal test
• Applied to the current state to test
• if the agent is in its goal
-Sometimes there is an explicit set of possible goal states.
(example: in Amman).
-Sometimes the goal is described by the properties
• instead of stating explicitly the set of states
• Example: Chess
• the agent wins if it can capture the KING of the opponent on next
move ( checkmate).
• no matter what the opponent does

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• A path cost function,
• assigns a numeric cost to each path
• = performance measure
• denoted by g
• to distinguish the best path from others
• Usually the path cost is
• the sum of the step costs of the individual actions (in the action list)

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• Together a problem is defined by
• Initial state
• Actions
• Successor function
• Goal test
• Path cost function
• The solution of a problem is then
• a path from the initial state to a state satisfying the goal test
• Optimal solution
• the solution with lowest path cost among all solutions

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Formulating problems
• Besides the five components for problem formulation
• anything else?
• Abstraction
• the process to take out the irrelevant information
• leave the most essential parts to the description of the states
( Remove detail from representation)
• Conclusion: Only the most important parts that are
contributing to searching are used

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Problem-Solving Agents
• agents whose task is to solve a particular problem (steps)
• goal formulation
• what is the goal state
• what are important characteristics of the goal state
• how does the agent know that it has reached the goal
• are there several possible goal states
• are they equal or are some more preferable
• problem formulation
• what are the possible states of the world relevant for solving the
problem
• what information is accessible to the agent
• how can the agent progress from state to state

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Example: Romania
• On holiday in Romania; currently in Arad.
• Flight leaves tomorrow from Bucharest
• Formulate goal:
• be in Bucharest
• Formulate problem:
• Initial states:
• actions:
• Transition Model
• Goal Test
• Path Cost
• Search solution :
sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest
• Execution

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Example: Romania

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Single-state problem formulation
• Initial State : In(Arad)
• Action : In(Arad){Go(Sibiu),Go(Timisoara),Go(Zerind)}
• Transition Model : Result(In(Arad),Go(Zerind))=In(Zerind)
• Goal Test : In(Zerind) ==In (Bucharest) Reached the Destination??
• Path Cost : Kms , Time in Hrs
• A solution is a sequence of actions leading from the initial state to a
goal state

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Example problems
• Toy problems
• those intended to illustrate or exercise various problem-solving methods
• E.g., puzzle, chess, etc.
• Real-world problems
• tend to be more difficult and whose solutions people actually care about
• E.g., Design, planning, etc.

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PROBLEM 2 : Toy problems
Example : Vacuum world

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Initial State : Any State can be Designed as Initial State
Action : L,R,S (If Large Environment UP & DOWN)
Transition Model : L->R – Successor – Suck the Dirt and Clean
R->L- Suck the Dirt and Clean
Goal Test : Check whether All the all Squares are Clean
Path Cost : Each Step cost 1(no of steps in a path)

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PROBLEM 3 :The 8-puzzle

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PROBLEM 3 :The 8-puzzle
Initial State : Any State can be Designed as Initial State
Action : L,R,S ,UP ,DOWN)
Transition Model : U->D,D->U,L->R,R->L Successor –
Action and State Returning State
Goal Test : Check whether it reaches the Goal State
Path Cost : Each Step cost 1(no of steps in a path)

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PROBLEM 4 : The 8-queens
Q
Q
Q
Q
Q
Q
Q
Q

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PROBLEM 4 : The 8-queens

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REAL WORLD PROBLEMS
• ROUTE FINDING PROBLEMS
• TOURING PROBLEMS
• TRAVEING SALES PERSON PROBLEM
• VLSI LAYOUT
• ROBOT NAVIGATION
• AUTOMATION ASSEMBLY SEQUENCING

UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdf

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UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdf