PARALLELIZATION OF
EXPERT SYSTEM
CS6260 Project Presentation
Vinay B Gavirangaswamy
What is Expert System?

Expert system is a program/application that mimics
decision taking ability of a domain expert.
Knowledge Base
Inference
Engine
Working Memory
User Interface
Domain Knowledge
Implementation Techniques – Buzz
words

Bayes Rule

Forward-Chaining

Bidirectional Search

Fuzzy-Set Theory

Causal Models

Goal-Driven Reasoning

Chronological Backtracking

Model Driven Reasoning

Control Rules

Opportunistic Search

Semantic Networks

Rules

Truth Maintenance Systems


Dempster-Shafer Uncertainty
Management
MYCIN
Production Systems



‘It’s a set of syntactic and semantic conventions that
make it possible to describe things’.
Syntax – Set of rules for combining symbols and
arrangements of symbols to form expression in a
representational language.
Semantic – Specifies how constructed expressions
should be interpreted.
Production Rules




Consists of premise-action pairs, for example
if P & ...&Pn,
1
then Q & ... & Qm
1
P’s – are called conditions or ‘left-hand side’
Q’s – are called conclusions or ‘right-hand side’
Working Memory (WM): It is to hold data, often in
the form of object-attribute-value triples.
The Behavior of the
Interpreter/Inference Engine
Interpreter or inference engine for a set of production
rules can be described in term of ‘recognize-act
cycle’.
1. Match the calling patterns of rules against
elements in working memory.
2. If there is more than one rule that could fire, then
decide which one to apply; this is called ‘conflict
resolution’.
3. Apply the rule, perhaps adding a new item to WM
or deleting an old one, and then go to step (1).
Forward Chaining



At the Global level of control, production rules are
driven forward.
Chains forward from condition that we know to be
true, towards conclusions which the facts allow us to
establish, by matching data in working memory
against the left-hand sides of the rules.
The mode of chaining describes the way rules are
activated by matching WM elements.
Related Work
Blue DRAGON




Analyzes Minimal Invasive (MIS) surgical tasks into
primary elements.
Kinematics and dynamics of left & right endoscopic
tools along with visual surgical scene is used as input to
derive model.
Decomposed surgical model consists of 30 fully
connected finite states Markov model. And its
equivalent grammar.
Objective learning curve is defined for expert surgeon
and trainee using a scoring function (MM). Evaluation is
based on distance between the two.
Blue DRAGON (Contd.)
Fully connected FSD for decomposing
MIS
Signature of forces, velocities
associated with different states
Red DRAGON (Blue DRAGON
Contd.)
CAD Design
Actual Machine
http://bionics.soe.ucsc.edu/research/surgical_index.html
Parallel Implementation of Rule-Based
Expert System

Or- parallelism: Each rule, whose head unifies with a fact, can be solved in parallel.

And-parallelism: Processes execute in parallel to solve each clause of the body.

Conflict resolution is the bottle-neck for parallel execution.

Rule Based system is described by following attributes



1. a set of parameters ; V  v , v ,..., vn ,
1 2
2. a set of inputs; V  v , v ,..., v  V ,
I
I1 I 2
Ik
3. a set of outputs; V  
v ,v
..., v 
V ,

O  O1 O 2,
Oh 

4. domain for parameters : D  D , D ,..., Dn . V, D  defines state space of system.
1 2
5. System state : At   a t , a t ,..., an t  , where a t   D is the value of v
i
1
2
i
6. a set of rules : R  R , R ,..., Rm
1 2
such that At  1  R At  and R : D  D so R is considered as transitio n function 







Knowledge Base Organization


Uses AND/OR graph formalism for the knowledge
network
Rules and parameters are organized to form a
knowledge network.
 v  IN  R  : a chain is formed between rule and parameter
j
i
 v  OUT  R  : a link is formed between rule and parameter
j
i
Knowledge Base Organization
(Contd.)
Knowledge Base of three rules
Knowledge Network
A
B
X
Y
Z
R100
R : IF A  2 THEN X : 2 * A and Y : 2  B
IF
i
R : IF F  G THEN Y : 2  3 and Z : 2 * 3
THEN
j
R : IF A  h Y  and X  6 THEN Q : 2 * F and R : 3
k
IF
THEN
A
B
X
Y
F
G
Y
Z
Q
R102
A
F
X
Y
Q
R
IF
THEN
F
G
Q
R
R
R102
X
Y
R100
A
Z
R101
B
F
G
Implementation on shared memory
MIMD machine





Implemented using tables, markers and message
broadcasting.
Only forward chaining is used in the inference
process.
B , B ,..., B
Shared memory is divided into number of banks
Each parameter is stored in memory bank and rule
is loaded into one processor.
Rules and parameters do not migrate to other
processors and memory banks during execution.
1
2
p
Implementation on shared memory
MIMD machine (Contd.)

1.
2.
Knowledge network is implemented through message broadcasting
Execute the actions of the rule being fired
If the execution of an action changes the value of parameter, then the
processor broadcasts a message, containing parameter-id, to the bus.
3.
Processor enters critical section to change value of the parameter.
4.
Processor resets the variable counter for the rule to 1.
5.
Pickup message broadcasted and update the local parameter values.
Rule base partitioning and allocation

Each input parameter is associated with probability
p l 
v
for the relative frequency
that appears in lth position
v
in input sequence
ij
ij
ij

 : is the priority of v  l  f P l 
k
ij

ij

ij
Use and to denote numerical values (min and max)
v
R
with rule
and the parameter
min/max equations for input and rules are written in
matrix form V  C  R  V and R  D  V  E
In iteration step R  D  V  E is computed first followed
by V  C  R
i

s
l 1
j
 t 1 
 t 1 
 t 1 
t 
Algorithm of partitioning rules for
parallel execution

1. Initialization 
 v  V , assign  to v ;
ij
I
ij
ij
 v  V  V ,   k  1;
ij
I

i
 R  R, assign priority 1 to R ;
i
i
2. Form the execution time vector E;
Form the min/max equations;
3. Use sequential iteration to solve the min/max equations for rule priorites;
Sort the rules into a list L according to their priorities;
L denotes the priority of the i rule in the list L.
4. For i  1 TO m DO m is the number of rules
th
i

Begin
d  inf L  R  for every R such that PROC  R ;
i
k
k
l
k
C RA    A /   
index  1;
FOR j  2 TO p DO P is the number of procs
Begin
d  inf L  R  for every R such that PROC  R ;
i
k
index  i;
END;
END;
Assign L to PROC ;
i
END.
index
k
l
It’s a greedy algorithm
Produces good but not
necessarily optimal rule
assignment to
multiprocessors
Cost of rule assignment
k

n
n
i 1
j 1
ij
i
j
Problem of partitioning
and assigning rules to
minimize cost C(RA) is
NP-complete.
Allocating parameter to memory
banks


Algorithm to allocate memory banks such that they
do not cause many memory access conflicts when
executed in parallel.
Problem of allocating parameter to memory banks
to minimize the reference cost is NP-complete and
O n 
developed algorithm has time complexity of
2
Parallel tools for implementing
Expert System




MultiLisp – interpreter written in Fortran and ported to
C extended with constructs for parallel execution and
shared memory.
Brain Aid Prolog – a parallel PROLOG environment for
Transputer systems of 1-512 processors.
BeBOP – language combines sequential and parallel
Logic Programming (LP), object oriented programming
and meta-level programming.
Multi-BinProlog –Linda-style parallel extension to
BinProlog.
References




Peter Jackson, Introduction to expert systems, Addison-Wesley
Longman Publishing Co., Inc., Boston, MA, 1986
Rosen J., J. D. Brown, L. Chang, M. Barreca, M. Sinanan, B.
Hannaford, The Blue DRAGON - A System for Measuring the
Kinematics and the Dynamics of Minimally Invasive Surgical Tools In–
Vivo, Proceedings of the 2002 IEEE International Conference on
Robotics & Automation, Washington DC, USA, May 11-15, 2002.
Rosen J., L. Chang, J. D. Brown, B. Hannaford, M. Sinanan, R. Satava,
Minimally Invasive Surgery Task Decomposition - Etymology of
Endoscopic Suturing, Studies in Health Technology and Informatics Medicine Meets Virtual Reality, vol. 94, pp. 295-301, IOS Press,
January 2003.
Tao Li, Parallel implementation of rule-based expert systems for
interactive applications, Parallel Computing Volume 10, Issue 3, May
1989, Pages 309–318