Applications of Propositional Reasoning: Diagnosis & Verificaiton

Slides:



Advertisements
Similar presentations
Model Checking Base on Interoplation
Advertisements

1 Reasoning with Promela Safety properties bad things do not happen can check by inspecting finite behaviours Liveness properties good things do eventually.
Completeness and Expressiveness
Propositional Satisfiability (SAT) Toby Walsh Cork Constraint Computation Centre University College Cork Ireland 4c.ucc.ie/~tw/sat/
Comparative Succinctness of KR Formalisms Paolo Liberatore.
PROOF BY CONTRADICTION
Algorithmic Software Verification VII. Computation tree logic and bisimulations.
CS 267: Automated Verification Lecture 8: Automata Theoretic Model Checking Instructor: Tevfik Bultan.
Computing Minimum-cardinality Diagnoses by Model Relaxation Sajjad Siddiqi National University of Sciences and Technology (NUST) Islamabad, Pakistan.
Justification-based TMSs (JTMS) JTMS utilizes 3 types of nodes, where each node is associated with an assertion: 1.Premises. Their justifications (provided.
UIUC CS 497: Section EA Lecture #2 Reasoning in Artificial Intelligence Professor: Eyal Amir Spring Semester 2004.
Software Engineering & Automated Deduction Willem Visser Stellenbosch University With Nikolaj Bjorner (Microsoft Research, Redmond) Natarajan Shankar (SRI.
Counting the bits Analysis of Algorithms Will it run on a larger problem? When will it fail?
Methods of Proof Chapter 7, second half.. Proof methods Proof methods divide into (roughly) two kinds: Application of inference rules: Legitimate (sound)
1 Partial Order Reduction. 2 Basic idea P1P1 P2P2 P3P3 a1a1 a2a2 a3a3 a1a1 a1a1 a2a2 a2a2 a2a2 a2a2 a3a3 a3a3 a3a3 a3a3 a1a1 a1a1 3 independent processes.
From Propositional Logic to PROLOG CSE P573 Applications of Artificial Intelligence Henry Kautz Fall 2004.
Everything You Need to Know (since the midterm). Diagnosis Abductive diagnosis: a minimal set of (positive and negative) assumptions that entails the.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering CSCE 580 Artificial Intelligence Ch.5 [P]: Propositions and Inference Sections.
Automated Reasoning Matt Whipple and Brian Vees. Overview What is automated reasoning? What is automated reasoning? Properties of inference procedures.
Search in the semantic domain. Some definitions atomic formula: smallest formula possible (no sub- formulas) literal: atomic formula or negation of an.
Knoweldge Representation & Reasoning
Last time Proof-system search ( ` ) Interpretation search ( ² ) Quantifiers Equality Decision procedures Induction Cross-cutting aspectsMain search strategy.
Model Checking LTL over (discrete time) Controllable Linear System is Decidable P. Tabuada and G. J. Pappas Michael, Roozbeh Ph.D. Course November 2005.
Winter 2012SEG Chapter 11 Chapter 1 (Part 2) Introduction to Requirements Modeling.
CS 4700: Foundations of Artificial Intelligence
Lectures 11 / 12 Knowledge Representation Propositional Logic CSE 573 Artificial Intelligence I Henry Kautz Fall 2001.
Applications of Propositional Logic
Theory of Computing Lecture 17 MAS 714 Hartmut Klauck.
3/11/2002copyright Brian Williams1 Propositional Logic and Satisfiability Brian C. Williams /6.834 October 7 th, 2002.
Logical Agents Logic Propositional Logic Summary
1 CS 4700: Foundations of Artificial Intelligence Bart Selman Module: Knowledge, Reasoning, and Planning Part 1 Logical Agents R&N:
Formal Verification Lecture 9. Formal Verification Formal verification relies on Descriptions of the properties or requirements Descriptions of systems.
Logical Agents Chapter 7. Knowledge bases Knowledge base (KB): set of sentences in a formal language Inference: deriving new sentences from the KB. E.g.:
- 1 -  P. Marwedel, Univ. Dortmund, Informatik 12, 05/06 Universität Dortmund Validation - Formal verification -
SAT-Based Model Checking Without Unrolling Aaron R. Bradley.
1 Propositional Logic Limits The expressive power of propositional logic is limited. The assumption is that everything can be expressed by simple facts.
9/18/2000copyright Brian Williams1 Propositional Logic Brian C. Williams J/6.834J October 10, 2001.
Propositional Logic Russell and Norvig: Chapter 6 Chapter 7, Sections 7.1—7.4.
Assumption-based Truth Maintenance Systems: Motivation n Problem solvers need to explore multiple contexts at the same time, instead of a single one (the.
Complexity ©D.Moshkovitz 1 Our First NP-Complete Problem The Cook-Levin theorem A B C.
NAND, NOR, and EXOR (more primitive logical gates) CS Computer Architecture David Mayer.
On the Relation Between Simulation-based and SAT-based Diagnosis CMPE 58Q Giray Kömürcü Boğaziçi University.
Logical Agents. Outline Knowledge-based agents Logic in general - models and entailment Propositional (Boolean) logic Equivalence, validity, satisfiability.
CS 4700: Foundations of Artificial Intelligence
Automatic Test Generation
Logics for Data and Knowledge Representation
Digital Logic.
From Classical Proof Theory to P vs. NP
Applications of Propositional Reasoning Systems
L is in NP means: There is a language L’ in P and a polynomial p so that L1 ≤ L2 means: For some polynomial time computable map r :  x: x  L1 iff.
CS 4700: Foundations of Artificial Intelligence
Recursive stack-based version of Back-chaining using Propositional Logic
Model-based Diagnosis: The Single Fault Case
(CSC 102) Discrete Structures Lecture 2.
Hard Problems Introduction to NP
CS 4700: Foundations of Artificial Intelligence
EA C461 – Artificial Intelligence Logical Agent
AI in Space – Lessons From NASA’s Deep Space 1 Mission
Logics for Data and Knowledge Representation
IS 2935: Developing Secure Systems
Alternating tree Automata and Parity games
CSE 20: Discrete Mathematics for Computer Science Prof. Shachar Lovett
Artificial Intelligence: Logic agents
Computer Security: Art and Science, 2nd Edition
“Definition” of Combinational
Introduction to Requirements Modeling
Our First NP-Complete Problem
Methods of Proof Chapter 7, second half.
Program correctness Axiomatic semantics
Habib Ullah qamar Mscs(se)
Presentation transcript:

Applications of Propositional Reasoning: Diagnosis & Verificaiton Henry Kautz

Diagnosis Problem: diagnosis a malfunctioning device Car Computer system Spacecraft where Design of the device is known We can observe the state of only certain parts of the device – much is hidden

Model-Based, Consistency-Based Diagnosis Idea: create a logical formula that describes how the device should work Associated with each “breakable” component C is a proposition that states “C is okay” Sub-formulas about component C are all conditioned on C being okay A diagnosis is a smallest of “not okay” assumptions that are consistent with what is actually observed

Consistency-Based Diagnosis Make some Observations O. Initialize the Assumption Set A to assert that all components are working properly. Check if the KB, A, O together are inconsistent (can deduce false). If so, delete propositions from A until consistency is restored (cannot deduce false). The deleted propositions are a diagnosis. There may be many possible diagnoses

Example: Automobile Diagnosis Observable Propositions: EngineRuns, GasInTank, ClockRuns Assumable Propositions: FuelLineOK, BatteryOK, CablesOK, ClockOK Hidden (non-Assumable) Propositions: GasInEngine, PowerToPlugs Device Description F: (GasInTank  FuelLineOK)  GasInEngine (GasInEngine  PowerToPlugs)  EngineRuns (BatteryOK  CablesOK)  PowerToPlugs (BatteryOK  ClockOK)  ClockRuns Observations:  EngineRuns, GasInTank, ClockRuns

Example Is F  Observations  Assumptions consistent? F  {EngineRuns, GasInTank, ClockRuns}  { FuelLineOK, BatteryOK, CablesOK, ClockOK }  false Must restore consistency! F  {EngineRuns, GasInTank, ClockRuns}  { BatteryOK, CablesOK, ClockOK }  false  FuelLineOK is a diagnosis F  {EngineRuns, GasInTank, ClockRuns}  {FuelLineOK, CablesOK, ClockOK }  false  BatteryOK is not a diagnosis

Complexity of Diagnosis If F is Horn, then each consistency test takes linear time – unit propagation is complete for Horn clauses. Complexity = ways to delete propositions from Assumption Set that are considered. Single fault diagnosis – O(n2) Double fault diagnosis – O(n3) Triple fault diagnosis – O(n4) …

Deep Space One Autonomous diagnosis & repair “Remote Agent” Compiled systems schematic to 7,000 var SAT problem Started: January 1996 Launch: October 15th, 1998 Experiment: May 17-21

Testing Circuit Equivalence Do two circuits compute the same function? Circuit optimization Is there input for which the two circuits compute different values? C C’ nand A B A B

Testing Circuit Equivalence nand D E A B A B

Symbolic Model Checking Any finite state machine is characterized by a transition function CPU Networking protocol Wish to prove some invariant holds for any possible inputs Bounded model checking: formula is sat iff invariant fails k steps in the future