Lecture 2: 11/4/1435

Problem Solving Agents

Lecturer/ Kawther Abas

k.albasheir@sau.edu.sa

363CS – Artificial Intelligence

Problem Solving Agents

Problem solving agent– One kind of agent– Find sequences of actions leading to desirable

environment states (goal)– Defining goal, makes it easier to define performance

measure, needed in definition of rational agent Problem formulation

– What aspects of the problem do we represent?– Representing actions, states, goals– These are typically represented in a programming

language, e.g., Python

Problem Formulation

Actions and states to considerStates possible world statesAccessibility the agent can determine via itssensors in which state it isconsequences of actions the agent knows theresults of its actionsLevels problems and actions can be specified atvarious levelsConstraints conditions that influence theproblem-solving processPerformance measures to be appliedCosts utilization of resources

Defined problems and solutions

Definition of a problem: collection of information that the agent will use to decide what to do– Start state– Actions

• Can specify as successor function or as operators• Actions that can be applied to a state

– Goal test• set of properties or abstract specification• tests a state to see if we have achieved goal

– Path cost function• assigns a numeric cost to a path, this is the sum of the step

costs (e.g., operator costs) along the path

Goal & Start States; Actions

Goal state– A state we want to achieve

• E.g., we want to win a game of chess• E.g., we want to be close to the wall in wall following

– Set of environment states that we want achieve Also have a start state State representation

– Means of defining states (e.g., in Python) of our environment Actions

– What can we do to a state?• In chess, we can move a player, constrained by the way a player can be

moved• With a robot-agent, we can move turn, translate (move forward)

– Actions cause transitions between states– Try to transform current state into goal state

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How to implement a general agent?

Overall Process

Search for goal

Input: start state, goal state

Computation

Output:

sequence of operators, and/or goal state

Input: actions knowledge

engineering

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Types of Problems

Generally assume environments are– Fully observable, deterministic, sequential,

static, discrete, single agent – If a problem can be solved directly or

analytically– Practically, don’t apply problem solving using

search– E.g., solving 2 + 2 = 4; solving for the roots of

a quadratic; programming a spreadsheet

Problem Solving Costs

Path Cost– Cost of operators along path to goal

Search Cost– Cost of expanding nodes in entire search process– E.g., cost of node expansion is 1

• search cost is number of nodes expanded in search process