ai- memory organisation systems

MEMORY ORGANIZATION SYSTEMS

Submitted by:

Ratika AgarwalRoll No.: 10Mtech-CSE001

FramesNetworks

Structuring of knowledge

Frames

Data structure to represent a mental model of a stereotypical situation. E.g.,

driving a car, Attending a meeting Eating in a restaurant.

A method of grouping and linking related chunks of knowledge.

Simple facts: Can be written in clausal form as independent units. Placed in a KB in any order.

Example: (Clauses in the KB)PROFESSION (bob,professor)

FACULTY (bob,engg.)

.

.

.

MARRIED (bob,sandy)

FATHER_OF (bob,sue,joe)

DRIVES (bob,buick)

OWNS (bob,house)

No particular order/groupingBob is repeated

Management of knowledge can become difficult.

Knowledge structuring and organization becomes a necessity.

In case, qty. of information becomes large and complex,

Network representation

Pieces of knowledge clustered together into groups.

Pictorial presentation of objects, their attributes and the relationships between them and other

entities.

Associative networks

Also called semantic networks. Directed graphs with labeled nodes and arcs or

arrows.

fly

bird tweety yellow

wingsHAS-PARTS

COLORA-KIND-OF

CAN

Here Bird is a class of objects. The class has some properties, and A specific member of class is Tweety.

Example:

Memory organization

A key to efficient processing.

Memory: Central to common sense behavior and Basis for learning.

1) Short Term Memory (STM):Only a few items at a time can be held.Perceptual information are stored directly.

2) Long Term Memory (LTM):Capacity for storage Very large and permanent.

Episodic Memory,Semantic memory (associative memory).

Episodic Memory Memory of autobiographical events that can be

explicitly stated. (time, places, associated emotions and other

contextual knowledge) Contains information about personal

experiences. E.g.,

HAM

Developed by John Anderson and Gordon Bower (1973).

Memory is organized as a network of propositional binary trees.

When a fact is to be inserted:

System parses the sentence and builds a tree representation.Nodes are assigned unique numbers.Links are labeled with some functions.

When a new sentence is to be inserted:

Parsed Formed into tree-like memory structures.Or integrated(if some relation exists).

21

22

past

23

3

bob

24

tall

8

9

1

park

2

past

7

3

bob

6

4touch

5flower

c

o

pstL

f

r

c

ps

f

e

t

C: Context for tree facte: set membershipF: a factL: a locationO: objectP: predicateR: relationS: subjectT: time

”In a park, bob touched a flower.” “Bob was tall.”

Common node is merged into a single node.

A FACT

When query is posed to HAM:

I. A probe is formed.

II. Matched against existing memory structures.

III. If found, the best match is selected.

IV. And query is answered.

Matching:

I. Leaf node is located in memory that match leaf node in the node.

II. Corresponding higher links are checked for the same labels in same order.

III. Based on recency of usage.

IV. Infrequent accessed nodes may be forgotten.

CYRUS Studies problems associated with the retrieval and

organization of reconstructive memory.

Stores episodes from the lives of former secretaries of state Cyrus Vance and Edmund Muskie.

Episodes are indexed and stored in LTM for subsequent use in answering queries posed in English.

Its memory model is a network consisting of E-MOPs.

E-MOPS

A frame-like node structure.

Indexed in memory by one or more distinguishing features: its type and its value.

E.g., there are basic E-MOPs for diplomatic meetings with foreign dignitaries, specialized political conferences, negotiations, state dinners…

Contains conceptual information of different categories of episodic events.

An example

$MEET : Diplomatic meeting EMOPInformation common to all diplomatic events.

Actor : Cyrus Vance Participants : foreign diplomats Topics : international contracts Actions : participants talk to each other Goals : to resolve disputed contract

E-MOP contains

The indices which index individual episodes.

Other E-MOPs which have become specializations of their parent E-MOPs.

An $MEET may be indexed by features:LocationActual topic discussedActual meeting participants.

Example

Indexes 2 event meetings

EV1 can be located from $4MEET event through : topic, participant’s nationality, participant’s occupations...

On insertion of new Diplomatic meetings: Integrated with the $MEET E-MOP as a separately indexed

event. Merged with another event to form a new specialized

meeting E-MOP.

On insertion of several events belonging to same EMOP category:

Common event features are used to generalize E-MOP. Specialization occurs on over-generalization.

Reorganization Continually occurs when facts are entered.

Prevents addition of excessive memory entries.

Causes forgetting when new structures are formed.

Working of CYRUS with ex. $MEET E-MOP contains content part of frame.

After a first meeting  Indices relevant and unique to that meeting are established. Pointers are set to the corresponding event.

If two new meetings have some features in common: A new sub-E-MOP would be formed, Indices are established and pointers are set to new E-MOP.

Summary :

CYRUS, a good example of ‘Intelligent’ memory organization.

Exhibits desirable features possessed by human memories.

E-MOPs organize events by indexing them according to their differences.

Similarities between events make up the generalized episode descriptions.