Artificial Intelligence Syllabus Introduction to AIProblem formulation Problem

Artificial Intelligence

Syllabus Introduction to AI-Problem formulation, Problem Definition -Production systems, Control strategies, Search strategies. Problem characteristics, Production system characteristics -Specialized production system - Problem solving methods - Problem graphs, Matching, Indexing and Heuristic functions -Hill Climbing-Depth first and Breath first, Constraints satisfaction - Related algorithms, Measure of performance and analysis of search algorithms

DEFINITION • Intelligence exhibited by machines or software. • A field of study which studies how to create computers and computer software that are capable of intelligent behavior • “The study and design of intelligent agents”, in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success • “The science and engineering of making intelligent machines"

APPLICATIONS • • Autonomous planning and scheduling Game playing Autonomous control Diagnostics Logistics planning Robotics Voice Recognition

Problem Characteristics • • Decomposable? Can partial steps be ignored or undone ? Predictable ? Is “good” solution easily recognizable ? Is Knowledge Base consistent ? How much Knowledge is needed ? Stand–alone vs. Inter–active.

Problem Definition • The solution of many problems can be described by finding a sequence of actions that lead to a desirable goal. Each action changes the state and the aim is to find the sequence of actions and states that lead from the initial (start) state to a final (goal) state.

Problem Solving Methodology: • • The followings are things to build a system to solve a particular problem Define the problem precisely Analyze the problem(SEARCH) Knowledge representation Choose the best problem solving techniques

Problem Formulation • Initial state : Any Configuration • Operator or successor function - A successor function is needed to move between different states. Representation of the successor function: • Conditions of applicability • Transformation function • State space - all states reachable from initial by any sequence of actions • Path - sequence through state space • Path cost - function that assigns a cost to a path. Cost of a path is the sum of costs of individual actions along the path • Goal test - test to determine if at goal state

Example

• State space : A state description specifies the location of each of the eight tiles and the blank in one of the nine squares. • Initial state : Any state can be designated as the initial state. • Successor function : ”Blank Move” – Condition: the move is within the board – Transformation: blank moves Left, Right, Up, or Down • Goal Test : This checks whether the state matches the goal state • Path cost : Each step costs 1, so the path cost is the number of steps in the path.

Problems in Text Book Question: A monkey is in a room with a crate, with bananas suspended just out of reach on the ceiling. He would like to get the bananas. Process: After several unsuccessful attempts to reach the bananas, the monkey walks to the box, pushes it under the bananas, climbs on the box, picks the bananas and eats them

Example: Initial State

Goal State

• Initial State: on(monkey, floor), on(box, floor), at(monkey, a), at(box, b), at(bananas, c), status(bananas, hanging). • Goal State: on(monkey, box), on(box, floor), at(monkey, c), at(box, c), at(bananas, c), status(bananas, grabbed).

Solution on(monkey, floor), on(box, floor), at(monkey, a), at(box, b), at(bananas, c), status(bananas, hanging) go(a, b) on(monkey, floor), on(box, floor), at(monkey, b), at(box, b), at(bananas, c), status(bananas, hanging) push(box, b, Y) Y=c on(monkey, floor), on(box, floor), at(monkey, Y), at(box, Y), at(bananas, c), status(bananas, hanging) climbon(box) on(monkey, box), on(box, floor), at(monkey, Y), at(box, Y), at(bananas, c ), status(bananas, hanging) grab(bananas) For the monkey to grab the bananas it must be in the same location, so the variable location Y inherits c. This creates a complete plan. Y=c on(monkey, box), on(box, floor), at(monkey, c), at(box, c), at(bananas, c), status(bananas, grabbed) GOAL

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