Real-Time%20Knowledge-Based%20Systems%20for%20Enterprise%20Decision%20Support%20and%20Systems%20Analysis

1
Real-Time Knowledge-Based Systems for Enterprise
Decision Support and Systems Analysis

• Albert M. K. Cheng
• Real-Time Systems Laboratory
• University of Houston, USA

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Motivations

• Real-time systems must meet stringent timing and
  logical correctness requirements, and must
  guarantee quality-of-service (QoS)
• Enterprise information systems (EISs) satisfy
  meet similar requirements but at a slower and
  more flexible scale
• Economic and organizational impact of EISs
  require rigorous specification and analysis of
  these systems

3
Presentation Outline

• Introduction to real-time systems (RTSs) and
  real-time rule-based systems (RTRBSs)s
• Using RTRBSs for decision support
• Modeling EISs as finite-state systems
• Modeling EISs as rule-based systems
• Specification, analysis, and verification

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A Real-Time System
A
Sensor input
Decision, action
X
Y
D
S
State
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Real-Time Scheduling

• Earliest-Deadline-First (EDF) scheduler is
  optimal in uniprocessor system (or single-person
  project)
• EDF scheduler is not optimal in multiprocessor
  system (multi-person project)
• Example task 1 2 3
  (2 processors)
• deadline 3 2
  2
• computation 3 1 1
• feasible schedule processor 1 1 1 1
• processor 2
  2 3
• time
  0 1 2 3

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Multiprocessors EDF is not optimal

• Example (continued)
• processor 1 2 1 1 1 Note each
  task must be
• processor 2 3
  executed sequentially
• time 0 1 2 3
• s
• Task 1 misses deadline at time 3 (it needs 1
  more time unit of computation from time 3 to 4)

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Enterprise Information System

• External inputs (may be from another EIS)
• Outputs from EIS (may be to another EIS)
• Communication between internal components of the
  EIS
• Feedback (as external inputs or feedback inputs)
  from previous outputs
• Data organization, mining, and validity

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Knowledge-Based Systems

• IF condition THEN action/decision
• Examples
• OPS5LHS condition ? RHS action/decision
• EQL action/decision if conditions

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OPS5 Knowledge-Based Systems

• Set of rules
• (p rule-name
• (condition-elem-1)
• (condition-elem-m)
• ?
• (action-1)
• (action-n)

• Database of assertions
• (class-name attribute-1 value-1)
• (class-name attribute-2 value-2)
• (class-name attribute-p value-p)

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Example Processing wind-speed information

• (p wind-scan an OPS5 rule
• (region-scan1 sensor high-wind-speed)
• positive condition element
• (region-scan2 sensor high-wind-speed)
• positive condition element
• (status-check status normal) positive
  condition el
• - (interrupt status on negative condition
  elem
• ltUninitialized-configurationgt positive
  condition el (configuration high-wind 0
• --gt
• (modify ltUninitialized-configurationgt
  high-wind 1)) action

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Example EQL Rule-Based Program

• ( 1 ) object_detected true IF sensor_a 1
  AND sensor_a_status good
• ( 2 ) object_detected true IF sensor_b
  1 AND sensor_b_status good
• ( 3 ) object_detected false IF sensor_a
  0 AND sensor_a_status good
• ( 4 ) object_detected false IF sensor_b
  0 AND sensor_b_status good

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Representing EIS as Finite-State Graph for Model
Checking
Is the finite-state graph a model of the
temporal logic formula?
EIS specification represented as a
labeled finite-state Graph (Kripke structure)
Safety assertion written as temporal logic formula
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Example EIS for processing bank
automated-teller-machine (ATM) transactions
(deposit functions only)

• System with 2 ATMs
• Ensure that 2 concurrent deposits to the same
  account at different locations yield correct
  result
• Solution requires enforcement of mutual
  exclusions
• Notations N no deposit
• T want to make deposit
• C making deposit

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Example EIS for processing bank
automated-teller-machine (ATM) transactions
(finite-state graph spec)
N1,N2
T1,N2
N1,T2
C1,N2
T1,T2
T1,T2
N1,C2
C1,T2
T1,C2
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Computation Tree Logic CTL

• Propositional, branching-time temporal logic
• Next-time operator X, Until operator U
• A(E)X f f holds in every (some) immediate
  successor of current state

• A(E)f1 U f2 for every (some) computation
  path, there exists an initial prefix of the path
  such that f2 holds at the last state of the
  prefix and f1 holds at all other states along the
  prefix

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Representing EIS as a Rule-Based System

• Execution of a rule-based system can be modeled
  by a finite-state graph
• A finite-state graph can derive a rule-based
  system
• An EIS can be modeled as a finite-state-graph,
  and hence as a rule-based system

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Example Information on machine operators
requiring mutual exclusion in an assembly line

• N operators in assembly line
• 1 assembly unit is shared
• Each operator spends only a fraction of time
  actually assembling components remainder of time
  obtaining components and waiting
• Ensure only 1 operator using assembly unit

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Example Solution to machine operators requiring
mutual exclusion in assembly line (rule-based
spec)

• PROGRAM mutual_exclusion_algorithm_a
• INIT
• request empty,
• ticked true,
• queue_head empty,
• queue_tail 0,
• queue_0 empty,
• queue_n_1 empty,
• timer 0

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Example Timing-based solution in EQL

• RULES
• ( rules to encode add-request-to-queue
  operations )
• queue_0 request ! queue_head 0 !
  queue_tail 1
• IF request ltgt empty AND
  queue_head empty
• AND queue_tail 0
• queue_n_1 request ! queue_tail 0
• IF request ltgt empty AND
  queue_tail n-1

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Example continued

• ( rule to simulate clock tick )
• timer timer - 1 ! ticked true
• IF tick true AND timer gt 0
• ( rules to encode grant operation to first
  request in queue )
• queue_head 1 ! timer (ml) DIV c1 1 !
  ticked false
• IF queue_head 0 AND queue_0 0
• AND timer lt 0 AND ticked true

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Example Continued

• queue_head 0 ! timer (ml) DIV c1 1 !
  ticked false
• IF queue_head n-1 AND queue_n_1 0
• AND timer lt 0 AND ticked true
• queue_head 1 ! timer (ml) DIV c1 1 !
  ticked false
• IF queue_head 0 AND queue_0 n-1
• AND timer lt 0 AND ticked true

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Example continued

• queue_head 0 ! timer (ml) DIV c1 1 !
  ticked false
• IF queue_head n-1 AND queue_n_1
  n-1
• AND timer lt 0 AND ticked true
• ticked false
• IF queue_head empty OR timer gt 0 OR
  ticked false
• OR queue_head queue_tail
• END.

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Analysis of Rule-Based Systems

• The RULES section is composed of a finite set of
  rules each of which is of the form
• a1 b1 ! a2 b2 ! ! am bm
• IF enabling condition
• VAR set of variables on left-hand side of
  the assignment, i.e., the ais
• VAL expressions on right-hand side of
  assignment, i.e., the bis
• EC enabling condition

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State Space Representation
C
A
B
L
I
D
J
M
E
F
P
K
H
N
G
FP2
FP3
FP1
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Problem Complexity

• In general, the analysis problem is undecidable
  if the program variables can have infinite
  domains, i.e., there is no general procedure for
  answering all instances of the decision problem.

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Proof Outline

• Any two-counter machine can be encoded by an
  equational rule-based program that uses only '
  and -' as operations on integer variables and
  gt', ' as atomic predicates such that a
  two-counter machine accepts an input if and only
  if the corresponding equational rule-based
  program can reach a fixed point from an initial
  condition determined by the input to the
  two-counter machine.

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Analysis Problem is Solvable for some Cases

• All the variables of an equational rule-based
  program range over finite domains.
• Set of variables in VAR and set of variables in
  VAL and EC are disjoint.
• Enabling conditions are mutually exclusive.
• Only constants are assigned to variables in
  VAR.

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Compatibility of Rules

• Let L_x denote the set of variables appearing in
  LHS of rule x.
• Two rules a and b are said to be compatible iff
  at least one of the following conditions holds

• (CR1) Test a and test b are mutually exclusive.
• (CR2) L_a and L_b are disjoint.
• (CR3) Suppose L_a and L_b are not disjoint .
  Then for every common variable v in L_a and L_b,
  the same expression must be assigned to v in both
  rule a and b.

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Special Form A

• Let L and T be sets of variables in VAR and EC of
  rules. A set of rules are in special form A if
  the following conditions hold
• (1) Constant terms are assigned to all the
  variables in L.
• (2) All of the rules are compatible pairwise.
• (3) L and T are disjoint.

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Example

• 1. a1 true IF b true AND c true
• 2. a1 true IF b true AND c false
• 3. a2 false IF c true
• Rules 1 and 2 are compatible by conditions CR1
  and CR3. Rules 1 and 3 are compatible by
  condition CR2. Rules 2 and 3 are compatible by
  condition CR2.

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General Analysis Strategy
Rule-based program
Special form recognizer
Simpler programs
No independent ruleset in special form
Independent rulsets in special form(s)
Rule rewriter
State-space analyzer
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Example

• input read(b, c)
• 1. a1 true IF b true AND c true
• 2.a1 true IF b true AND c false
• 3.a2 false IF c true
• 4.a3 true IF a1 true AND a2 false
• 5.a4 true IF a1 false AND a2 false
• 6.a4 false IF a1 false AND a2 true

• input read(b, c)
• 1. a1 true IF b true AND c true
• 2.a1 true IF b true AND c false
• 3.a2 false IF c true

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Applications of Analysis Tools

• Cryogenic Hydrogen Pressure Malfunction Procedure
  in the Pressure Control System of the Space
  Shuttle Vehicle
• Integrated Status Assessment Expert System
• Fuel Cell Expert System
• Orbital Maneuvering and Reaction Control System

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Ongoing Work

• Modeling of EISs and other non-rule-based systems
  using rule-based analytic techniques
• Automating this modeling by developing mechanical
  encoding algorithms from EIS specification to
  rule-based representation
• Applying real-time scheduling principles to a
  real Houston company with data-intensive team
  projects

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New Textbook

• Albert Cheng - Real-Time Systems Scheduling,
  Analysis, and Verification (John Wiley Sons)
  ISBN 0471-184063, 2002. www.cs.uh.edu/acheng/
  acheng.html
• For senior-level undergraduate/first-year
  graduate courses in real-time systems, embedded
  systems (software and hardware) engineering, and
  formal methods. Serves as a supplement to courses
  in operating systems and system design, as well
  as a reference for practitioners and researchers.