Planning Techniques Planning:The problem of finding some action to achieve some goal System’s Plan:The sequence of such actions is called System’s Plan

Means – Ends Analysis (MEA) MEA is a simple planning method in AI. “It is a technique which, rather than blindly searching through all possible actions, focuses on actions that reduce the difference between the current state an the target state”

Algorithm: To find-plan (initial state, Target state) If all the goals in target state are true in initial state then succeed Otherwise 1.Select an unresolved goals fro target state 2.Find an action that adds goal to the current-state 3.Enable action by finding a plan (PREPLAN) that activeness its pre conditions i.e., find-plan (initial state, pre conditions.) Let Mid-state 1 be the result of applying that plan to initial state. 4.Apply action to Mid state 1 to give Mid state 1 to give Mid state 2 5.Find a plan (post plan) from Mid state 2 to target state 6.Return a plan consisting of Preplan, Action, and Post Plan

Example: “To pick a book from the Cupboard” Preplan:To walk to the Cupboard Action:To pick the book from Cupboard Post Plan:To walk back to the Original place. Target State Initial ActionPost Plan Mid State ‘2’ Mid State ‘1’ State Preplan

Example Target:Box with Sarah in Room A, with her ROBOT Initial State: Sarah and Robot in Room A Door between A and B closed Box in Room B Problem Representation As a List: (All are true) [at(robot, Room A), at (Box, Room B), at (Sarah, Room A), door-closed(Room A, Room B)] [T,T,T.T] (All are true) AB Box Sarah Sarah’s Robot

Target: [at(Box, Room A), at (Sarah, Room A), at (robot,( Room A), door-closed(Room A, Room B)] We need to define certain actions: Operators Open(Room B, Room A), move (Room A, Room B), Carry(Room B, Room A, Box), close(Room B, Room A). Box AB Open the door move pick break

Each Operator will have certain  Pre conditions to be applied  And will have have certain effects ExamplePre-conditionsEffects Open(Room A, Room Bat(Robot, Room A door-closed(Room A, Room B)door-open (Room A, Room B). Move(Room A, Room B)at(Robot, Room A) door-open(Room A, Room B)at(Robot, Room B)

Production Systems -Important Tool for:-search algo -modeling human problem solving -Consist of:-production rules -working memory -recognise-act control Cycle.

Production Rules (Productions) -Is a Condition – Action Pair Defines the associated problem-solving Step. Pattern that Determines when To apply rule

Working Memory -Contains the description of current state -This is compared with the production to determine the type of action required. (Always changes when productions are applied)

Recognise – Act Cycle Initialise Working Memory Productions - Conditions Conflict Set Conflict Resolution (if the two match) (one pattern taken at a time) (Enabled) Production - (Fired)

Conflict Set Productions can only fire when conditions in production- conditions are matched in the working memory (WM). If they match a subset of productions is formed called Conflict Set. ”Part of Production That Are Enabled” Conflict Resolution: It chooses a rule from conflict set for firing. Selection of any rule may be based on complex rule selection heuristics.

Pure Production Systems Systems in which no conflict resolution remains. All conflicts are resolved A Production System WM Patterns G A1 GA2 - Pattern Action

Example: Production Set: 1.baab 2.caac 3.cbbcThe methods condition # 3 Working Memory=acbcc Pattern Conflict Set=‘3’ Rule fired=3 abccc

Production Set: 1.p ^ qgoal 2.r ^ sp 3.w ^ rq 4.t ^ uq 5.Vs 6.Startv ^ y ^ q Data Driven Search in Production System

Functions of Robot Arm:- goto(X,Y,Z) pickup(W) Put down(W) Stack(U,V) Unstack(U,V) Predicate Calculus and Planning State: location(A, X,Y,Z) on(b, a) clear(b) gripping( ) On table(c) e d b ac Block World

1.Ontable(a)on(b,a)clear(b) 2.Ontable(c)on(e,d)clear(c) 3.Ontable(d)gripping(\)clear(e) State of the World Representation e d b ac State 1 Ontable(a)on(b,a)clear(b) Ontable(c)on(c)clear(d) Ontable(d)gripping(e)clear(e) e d b ac State 2

We define Pre conditions(P) Add(A) Delete(D) e.g., Using Production System P:gripping()^clear(x) ^ on table(x) may not be necessary A:gripping(x) D:on table(x)^ gripping(). may be replaced by on (X,Y) Pick up(X)

1.Ontable(a)on(b,a)clear(b) 2.Ontable(c)on(e,d)clear(c) 3.Ontable(d)gripping(\)clear(e) State of the World Representation e d b ac State 1 Ontable(a)on(b,a)clear(b) Ontable(c)on(c)clear(d) Ontable(d)gripping(e)clear(e) e d b ac State 2

If in order to achieve some goal, other increases the computation sub goals are distributedload may send misleading signals to the control section Goals Program of Incompatible Sub goals e d b ac On(b,a)^ on(a,c)^ on*d,e) Already truetrue Note: In order to reach goal on (b,a) has to be undone and then done again e d b a c Goal 1 Incompatible Goals

Black Board Architecture This extends production systems Organises Working Memory in to Modules It lumps separate “Production Rules” into single AGENTS

Black Board Architecture Is used when coordination of multiple processes or knowledge sources is required Example Multiple Sensing Problem Sensor S1:Alone does not significant information

Black Board: Is a central global database for the communication of independent asynchronous knowledge sources focusing on related aspects of a particular problem. Knowledge Source: (KS) Gets data from the black board Processes the data Returns it to the black board

Summary Of The System Knowledge Sources: Contains a set of independent modules called knowledge sources that contains the system domain special knowledge Black Board: A Black Board, which is the shared data structure through which the knowledge sources communicate with each other Control System: A control system which determines the order in which KSs will operate on the entries o the Board

Hearsay – II (Erman et.al 1980) One of the first developed project AIMED at speech recognition system