1. Lecture 2: 11/4/1435 Problem Solving Agents Lecturer/ Kawther Abas k.albasheir@sau.edu.sa 363CS – Artificial Intelligence

2. Problem Solving Agents 4 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 4 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

3. Problem Formulation Actions and states to consider States possible world states Accessibility the agent can determine via its sensors in which state it is consequences of actions the agent knows the results of its actions Levels problems and actions can be specified at various levels Constraints conditions that influence the problem-solving process Performance measures to be applied Costs utilization of resources

4. 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

5. Goal & Start States; Actions 4 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 4 Also have a start state 4 State representation –Means of defining states (e.g., in Python) of our environment 4 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

6. 6 How to implement a general agent?

7. Overall Process Search for goal Input: start state, goal state Computation Output: sequence of operators, and/or goal state Input: actions knowledge engineering Designer/ engineer

8. Types of Problems 4 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

9. Problem Solving Costs 4 Path Cost –Cost of operators along path to goal 4 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 Problem Formulation Actions and states to consider statespossible world states accessibilitythe agent can determine via its sensors in which state it is consequences of actionsthe agent knows the results of its actions levelsproblems and actions can be specified at various levels constraintsconditions that influence the problem-solving process performancemeasures to be applied costs utilization of resources