Table Of ContentDaniel P. Miranker
Department of Computer Sciences
The University of Texas at Austin
TREAT: A New and Efficient
Match Algorithm for AI
Production Systems
Pitman, London
Morgan Kaufmann Publishers, Inc., San Mateo, California
PITMAN PUBLISHING
128 Long Acre, London WC2E 9AN
A Division of Longman Group UK Limited
© Daniel P. Miranker 1990
First published 1990
Available in the Western Hemisphere from
MORGAN KAUFMANN PUBLISHERS, INC.,
2929 Campus Drive, San Mateo, California 94403
ISSN 0268-7526
British Library Cataloguing in Publication Data
Miranker, Daniel P.
TREAT: a new and efficient match algorithm
for AI production systems.—(Research
notes in artificial intelligence,
ISSN 0268-7526).
1. Expert systems. Design. Algorithms.
Treat
I. Title II. Series
658.5'3
ISBN 0-273-08793-2
Library of Congress Cataloging in Publication Data
Miranker, Daniel.
Treat: a new and efficient match algorithm for AI production
systems / Daniel Miranker.
p. cm.—(Research notes in artificial intelligence)
Bibliography: p.
ISBN 0-934613-71-0 (Morgan Kaufmann)
1. Expert systems (computer science) 2. Algorithms.
3. Artificial intelligence. 4. Parallel processing (electronic
computers) I. Title. II. Series.
QA76.76.E95M57 1990
006.3—DC19
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List of Figures
Figure 2-1 An Example Production Rule 5
Figure 2-2 An Example Production Rule with a Negated Condition 6
Element
Figure 2-3 The Generalized Structure of a Shared Memory Machine 9
Figure 3-1 Production System Rule as Database Query 13
Figure 3-2 Correspondence of Production Systems to Database Systems 14
Figure 3-3 Black Box View of a Production System Algorithm 15
Figure 3-4 RETE Match Network for the Rule in Figure 2-1 19
Figure 3-5 Initial State of the RETE Network 21
Figure 3-6 Activity of the RETE Match During an Addition 22
Figure 3-7 Activity of the RETE Match During a Deletion 23
Figure 4-1 Redundant Storage of State in Different Beta-Memories 26
Figure 4-2 Rule System Displaying Negated Condition Element Problem 28
Figure 4-3 Abstract Algorithm Illustrating TREAT 30
Figure 4-4 Initial State as Stored by TREAT 31
Figure 4-5 Activity of TREAT During an Addition 32
Figure 4-6 Activity of TREAT During a Deletion 33
Figure 4-7 Counting the Comparisons for TREAT 38
Figure 4-8 Sizing the Beta-memories 39
Figure 4-9 Measurements of the Number and Type of Conditions per Rule 41
Figure 4-10 Summary of the Gross Characteristics of the Studied Systems 43
Figure 4-11 The Number of Comparisons Required to Compute Variable 45
Bindings for Each OPS5 Implementation
Figure 5-1 Functional Division of the DADO Tree 51
Figure 5-2 Hyper-H Embedding of a Binary Tree 52
Figure 5-3 The Leiserson Chip Design 52
Figure 5-4 Two Leiserson Chips Hooked Together 52
Figure 5-5 The DADOl Prototype Processing Element 56
Figure 5-6 The DAD02 Prototype Processing Element 56
Figure 5-7 Queuing Model of DADO 1 a (no buffering) 59
Figure 5-8 Queuing Model of DADO 1 b (buffering) 60
Figure 5-9 Queuing Model of DAD02a (no buffering) 61
Figure 5-10 Queuing Model of DAD02b (buffering) 62
Figure 5-11 Statistics Characterizing the DADO Instruction Steam 62
Figure 5-12 Passive Queue Construct 63
Figure 5-13 DADO SIMD Instruction-Stream Generator Model 64
Figure 5-14 RESQ Queuing model of a DADOl PE 65
Figure 5-15 RESQ Queuing model of a DAD02 PE 65
Figure 5-16 Throughput Results for the Four DADO Models 66
Figure 5-17 Throughput vs. Length of Data Dependent Operations 67
Figure 5-18 Performance of Deeper DADO Trees 68
Figure 5-19 Software Layering for the DADO Architecture 69
Figure 5-20 Illustration of Tree Neighbor Communication 72
Figure 5-21 Sequentially Loading DADO 75
Figure 5-22 Associative Probing 76
Figure 5-23 Locus of Control Among PEs 78
Figure 6-1 NETL Hardware Organization 82
Figure 6-2 Organization of a Connection Machine Processing Element 86
Figure 6-3 Alpha Operator Applied to Xappings 97
Figure 6-4 The Beta Operator 98
Figure 7-1 Gupta's Production System Machine 104
Figure 7-2 Expected Speed From Gupta's Production System Machine 104
Figure 7-3 Proportion of Time Spent in Each Phase of the Production 110
System Cycle, Using TREAT-OPS5 and Full Distribution
Partitioning
Figure 7-4 Speed-Up of Partial Match 111
Figure 7-5 Size per Cycle of the Intersection of Affect Set and Active Set 113
Figure 7-6 Speed-up by Partition 114
Figure 7-7 Speed-up by PEs per Partition 117
Figure 7-8 Waltz Speed-Ups By Number of PEs 119
Figure 7-9 The Performance, Actual and Predicted, of Three OPS5
Implementations
X
Acknowledgements
I would like to thank Sal Stolfo, my thesis advisor, for thinking of the DADO machine in
the first place and for guiding my work, also for demonstrating the value of honesty and
integrity, and not least, for having enough faith and trust in me to give me enough rope to hang
myself.
Kudos for Rick Reed, for his great contributions toward the implementation of OPS5 on the
DADO machine and in the process painstakingly uncovering the bugs in everyone else's work,
including my own.
The DADO project is a large project - Andrew Comas, Eugene Dong, Zdenek Radouch and
Jody Weiss were responsible for the construction of the DAD02. My fellow graduate students
on the project, Mark Lerner, Andy Lowrie, Russell Mills, Al Pasik, Steve Taylor and Michael
van Beima and staff members Rick Reed, Lee Woodbury and Philip Yuen, as well as my
officemate Terry Boult, all contributed to many lively discussions about the DADO project.
Many of the ideas presented in this thesis arose from these discussions.
I am grateful for the editorial help provided by Pandora Setian, Russell Mills and especially
my sister-in-law Cathy Miranker, with their help the document as well as my own ability to
write has been greatly improved. Graphs of many of the measurements were done with help
from Matthew Kallis.
Last I am most thankful for the loving friendship and moral support provided by my wife
Valin.
XI
Abstract
Due to the dramatic increase in computing power and the concomitant decrease in
computing cost that has occurred over the last decade, many researchers are attempting to
design computing systems to solve complicated problems or execute tasks that have in the past
been performed by human experts. The focus oi Knowledge Engineering is the construction of
such complex "expert system" programs.
This book will describe the architecture and the software systems embodying the DADO
machine, a parallel tree-structured computer designed to provide significant performance
improvements over serial computers of comparable hardware complexity in the execution of
large expert systems implemented in production system form.
The central contribution of this book is a new match algorithm for executing production
systems, TREAT, that will be presented and comparatively analyzed with the RETE match
algorithm. TREAT, originally designed specifically for the DADO machine architecture can
handle efficiently both temporally redundant and nontemporally redundant production system
programs. Although the development of the algorithm was motivated by the inadequacies of
the parallel versions of existing production system algorithms, it is shown that the TREAT
algorithm performs better than the best known sequential algorithm, the RETE match, even on
a sequential machine.
Xlll
In memory of my mother, Phyllis Miranker
1 Introduction
1.1 The Problem
Since its inception, the ultimate goal in the field of artificial intelligence (AI) has been to create
a machine capable of learning and general problem solving. This goal has proven to be
difficult and elusive. Early successes in AI resulted from considering a restricted set of
problems in restricted domains; for example, question answering in a blocks world and solving
analogy questions on IQ tests [55]. These successes led researchers to consider "real world"
domains that ordinarily are in the province of trained human experts, but are still narrow in
scope.
In the last decade researchers have succeeded in creating expert programs (or expert
systems) that are capable of performing medical diagnosis [76], discovering mineral deposits
[11] and analyzing electronic circuits, to name a few. The heart of these systems is a
knowledge base, containing a large collection of facts, definitions, procedures and heuristic
"rules of thumb"acquired from a human expert.
More recently researchers have formalized the techniques involved in the development of
expert systems and have implemented computer tools and specialized languages [20, 9] that
facilitate the creation of expert systems. ACE, a system that recommends preventive
maintenance on telephone cables [96], and Rl/XCON, a system that configures VAX
computers [49] are two systems, both in commercial use, written directly in an expert system
language. Text books have been written so that knowledge engineering may be taught at the
undergraduate level [97, 8]. Knowledge engineers are the intermediary between the expert and
the system; they extract, formalize, represent, and test the relevant knowledge within a
computer program. In consequence expert systems are becoming increasingly important in the
commercial environment. Just as more conventional computer technologies offer the potential
for higher productivity in the blue-collar work force, it appears that AI expert systems will
offer the same productivity increase in the white-collar work force. Articles on AI and expert
systems continually published in the business pages of newspapers and magazines
[54, 3] substantiate this. Two independent market analyses estimate that AI-related business
will grow to nearly $10 billion a year by 1990 [4, 70].
Although the dramatic increase in computer power, available at ever decreasing prices, has
made the development of expert systems possible, these systems continue to tax the resources
of even large general-purpose computer systems. The benchmark timings of even the smallest
student expert-system projects, reported in later chapters of this book, require almost a CPU
minute on an IBM 4381 mainframe computer. Large scale expert systems require tens or
hundreds of times that amount. The lengthy response times for these systems certainly
frustrate system developers and perhaps impede the development of expert-system programs.
1
This book presents a two-fold attack on improving the speed of expert systems based on the
production system paradigm. The primary motivation behind the entire work is to investigate
the applicability of parallel computation to the production system problem. In that vein, the
specification, performance analysis and implementation of the DADO1 machine, a massively
parallel tree-structured computer, as well as its layered programming model are part of this
book. Two DADO prototypes have been built. The DADOl, a 15 processor machine, has
been operational since the spring of 1983. The second larger prototype, DAD02, contains
1,023 8-bit processors and has an aggregate capacity of 570 MIPS and 16 megabytes of RAM
storage. DAD02 has been operational since the fall of 1985. The DAD02 is reliable. Smaller
copies of DAD02, with 31 to 63 processors, are presently being used outside of Columbia
University by researchers at AT&T and the Fifth Generation Computer Corp.
The DADO serves as an environment that structures the development of parallel algorithms
for production systems. Since its development over 10 years ago, the RETE match algorithm
[20] has been assumed to be the best algorithm for the execution of production systems. This
assumption was questioned in a conjecture made by McDermott et al. [52]. An early study of
DADO [24] determined that parallel versions of the RETE match were inappropriate as
algorithms for the DADO machine. Motivated by the constraints imposed by the DADO
model, a new production system matching algorithm called TREAT is presented and analyzed.
A serendipitous and exciting result is that even in a sequential environment, a comparative
empirical analysis of TREAT and RETE shows that TREAT often outperforms the RETE
match often by greater than two to one. This analysis substantiates McDermott's conjecture
and forms the basic contribution of this thesis.
1.2 Outline of the Book
The preliminary chapter of the book contains background material. Production systems are
defined and the OPS5 production system language syntax is described. Since the motivation of
the work is the introduction of parallel execution of expert systems, a taxonomy of parallel
computers is also presented.
The next chapter, chapter three, details the algorithmic considerations of production system
interpreters, particularly as they relate to parallelism and also how they relate to relational
database systems. Chapter three suggests production system interpreters may be decomposed
into three nearly independent algorithmic aspects: low-level matching, partitioning and
synchronization. The problem of optimizing the execution of production systems has garnered
a great deal of attention in the community. These aspects are explained in detail and a synopsis
of work related to each aspect is presented. The algorithmic aspect central to the work is the
low-level matching issue. Since its development in 1974, the RETE match [21] has been
commonly assumed to be the best algorithm to be used to implement production system
interpreters. Chapter three includes a detailed description of the RETE match.
!DADO is a name not an acronym.
2
Chapter four presents the TREAT algorithm. Specific mention is made of how the strong
points of the RETE match in a sequential environment become troublesome in a parallel
environment. These observations coupled with ideas related to database query optimization
form the basis of the TREAT match algorithm. An interpreter for the OPS5 production system
language was implemented using TREAT. Two variations of the TREAT algorithm that
incorporate optimization techniques adapted from relational databases were also implemented.
Chapter four concludes with the presentation of the comparative performance of the three
versions of TREAT and the widely released implementation of OPS5 that is based on the
RETE match.
A developer of an expert system could improve the performance of his system by buying a
larger more expensive conventional computer. Another approach is to augment an inexpensive
workstation with a special-purpose coprocessor able to accelerate his application. The DADO
machine and its system architecture is intended to be such a coprocessor.
Chapter five, presenting the DADO system architecture, has three major components. First
is a description of how the the DADO architecture is capable of improving production system
performance by spawning large numbers of independent matching processes among its many
processors. Second, the execution model of the DADO machine, particularly as it pertains to
communication, is quite unusual. The second part of chapter five includes a performance
analysis of four alternative communication schemes. The data to drive this analysis was
derived from DADOl and used to optimally design the DAD02 machine. The third part of
chapter five is a detailed explanation of the layered programming environment for DADO and
in particular the parallel systems programming language for the machine.
Chapter six describes other parallel AI efforts. Some criticism of each of the machine
proposals is included, although a detailed analysis of these other machines is beyond the scope
of this book.
Chapter seven presents an outline of the implementation of TREAT on the DADO, related
work in the field of parallel implementations of production systems and empirical results that
form a basis to predict the performance of TREAT on the DADO machine.
1.3 Digest of Conclusions
1. The development of the TREAT production system matching algorithm provides
the opportunity to double the performance of forward chaining production
systems.
2. TREAT is demonstrated to be faster on serial computers than RETE, the best
known production system algorithm to date.
3. The DADO architecture contains a unique execution model where parallel
processing is performed by independent instruction streams, (MIMD
processing2) but the synchronization of communication operations make DADO
2MIMD and SIMD are defined in section 2.2.2.
3
