Scalable Knowledge Representation and Reasoning Systems - PowerPoint PPT Presentation


PPT – Scalable Knowledge Representation and Reasoning Systems PowerPoint presentation | free to view - id: 814b07-YTJlM


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation

Scalable Knowledge Representation and Reasoning Systems


Scalable Knowledge Representation and Reasoning Systems Henry Kautz AT&T Shannon Laboratories Introduction In recent years, we've seen substantial progress in scaling ... – PowerPoint PPT presentation

Number of Views:65
Avg rating:3.0/5.0
Slides: 65
Provided by: Henr1224


Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Scalable Knowledge Representation and Reasoning Systems

Scalable Knowledge Representation and Reasoning
  • Henry Kautz
  • ATT Shannon Laboratories

  • In recent years, we've seen substantial progress
    in scaling up knowledge representation and
    reasoning systems
  • Shift from toy domains to real-world applications
  • autonomous systems - NASA Remote Agent
  • just in time manufacturing - I2, PeopleSoft
  • deductive approaches to verification - Nitpick
    (D. Jackson), bounded model checking (E. Clarke)
  • solutions to open problems in mathematics - group
    theory (W. McCune, H. Zhang)
  • New emphasis on propositional reasoning and search

Approaches to Scaling Up KRR
  • Traditional approach specialized languages /
    specialized reasoning algorithms
  • difficult to share / evaluate results
  • New direction
  • compile combinatorial reasoning problems into a
    common propositional form (SAT)
  • apply new, highly efficient general search

SAT Encoding
Combinatorial Task
SAT Solver
  • Compare with use of linear / integer programming
  • emphasis on mathematical modeling
  • after modeling, problem is handed to a state of
    the art solver
  • Compare with reasoning under uncertainty
  • convergence to Bayes nets and MDP's

Would specialized solver not be better?
  • Perhaps theoretically, but often not in practice
  • Rapid evolution of fast solvers
  • 1990 100 variable hard SAT problems
  • 1999 10,000 - 100,000 variables
  • competitions encourage sharing of algorithms and
  • Germany 91 / China 96 / DIMACS-93/97/98
  • Encodings can compensate for much of the loss due
    to going to a uniform representation

Two Kinds of Knowledge Compilation
  • Compilation to a tractable subset of logic
  • shift inference costs offline
  • guaranteed fast run-time response
  • E.g. real-time diagnosis for NASA Deep Space One
    - 35 msec response time!
  • fundamental limits to tractable compilation
  • Compilation to a minimal combinatorial core
  • can reduce SAT size by compiling together problem
    spec control knowledge
  • inference for core still NP-hard
  • new randomized SAT algorithms - low exponential
  • E.g. optimal planning with 1018 states!

  • I. Compilation to tractable languages
  • Horn approximations
  • Fundamental limits
  • II. Compilation to a combinatorial core
  • III. Improved encodings
  • Compiling control knowledge
  • IV. Improved SAT solvers
  • Randomized restarts

I. Compilation to Tractable Languages
Expressiveness vs. Complexity Tradeoff
  • Consider problem of determining if a query
    follows from a knowledge base
  • KB q ?
  • Highly expressive KB languages make querying
  • ( ignition_on engine_off ) ?
  • ( battery_dead V tank_empty )
  • require general CNF - query answering is
  • Less expressive languages allow polynomial time
    query answering
  • Horn clauses, binary clauses, DNF

Tractable Knowledge Compilation
  • Goal guaranteed fast online query answering
  • cost shifted to offline compilation
  • Exact compilation often not possible
  • Can approximate original theory
  • yet retain soundness / completeness for queries
  • (Kautz Selman 1993, 1996, 1999 Papadimitriou

expressive source language
tractable target language
Example Compilation into Horn
  • Source clausal propositional theories
  • Inference NP-complete
  • example (a V b V c V d)
  • equivalently (a b) ? (c V d)
  • Target Horn theories
  • Inference linear time
  • at most one positive literal per clause
  • example (a b) ? c
  • strictly less expressive

Horn Bounds
  • Idea compile CNF into a pair of Horn theories
    that approximate it
  • Model truth assignment which satisfies a theory
  • Can logically bound theory from above and below
  • LB S UB
  • lower bound fewer models logically stronger
  • upper bound more models logically weaker
  • BEST bounds LUB and GLB

Using Approximations for Query Answering
  • S q ?
  • If LUB q then S q
  • (linear time)
  • If GLB q then S q
  • (linear time)
  • Otherwise, use S directly
  • (or return "don't know")
  • Queries answered in linear time lead to
    improvement in overall response time to a series
    of queries

Computing Horn Approximations
  • Theorem Computing LUB or GLB is NP-hard
  • Amortize cost over total set of queries
  • Query-algorithm still correct if weaker bounds
    are used
  • anytime computation of bounds desirable

Computing the GLB
  • Horn strengthening
  • r ? (p V q) has two Horn-strengthenings
  • r ? p
  • r ? q
  • Horn-strengthening of a theory conjunction of
    one Horn-strengthening of each clause
  • Theorem Each LB of S is equivalent to some
    Horn-strengthening of S.
  • Algorithm search space of Horn-strengthenings
    for a local maxima (GLB)

Computing the LUB
  • Basic strategy
  • Compute all resolvents of original theory, and
    collect all Horn resolvents
  • Problem
  • Even a Horn theory can have exponentially many
    Horn resolvents
  • Solution
  • Resolve only pairs of clauses where exactly one
    clause is Horn
  • Theorem Method is complete

Properties of Bounds
  • GLB
  • Anytime algorithm
  • Not unique - any GLB may be used for query
  • Size of GLB ? size of original theory
  • LUB
  • Anytime algorithm
  • Is unique
  • No space blow-up for Horn
  • Can construct non-Horn theories with
    exponentially larger LUB

Empirical Evaluation
  • 1. Hard random theories, random queries
  • 2. Plan-recognition domain
  • e.g. query (obs1 obs2) ? (goal1 V goal2) ?
  • Time to answer 1000 queries
  • original with bounds
  • rand100 340 45
  • rand200 8600 51
  • plan500 8950 620
  • Cost of compilation amortized in less than 500

Limits of Tractable Compilation
  • Some theories have an exponentially-larger
    clausal form LUB
  • QUESTION Can we always find a clever way to keep
    the LUB small (new variables, non-clausal form,
    structure sharing, ...)?
  • Theorem There do exist theories whose Horn LUB
    is inherently large
  • any representation that enables polytime
    inference is exponentially large
  • Proof based on non-uniform circuit complexity -
    if false, polynomial hierarchy collapses to ?2

Other Tractable Target Languages
  • Model-based representations
  • (Kautz Selman 1992, Dechter Pear 1992,
    Papadimitriou 1994, Roth Khardon 1996, Mannila
    1999, Eiter 1999)
  • Prime Implicates
  • (Reiter DeKleer 1987, del Val 1995, Marquis
    1996, Williams 1998)
  • Compilation from nonmonotonic logics
  • (Nerode 1995, Cadoli Donini 1996)
  • Similar limits to compilability hold for all!

Truly Combinatorial Problems
  • Tractable compilation not a universal solution
    for building scalable KRR systems
  • often useful, but theoretical and empirical
  • not applicable if you only care about a single
    query no opportunity to amortize cost of
  • Sometimes must face NP-hard reasoning problems
    head on
  • will describe how advances in modeling and SAT
    solvers are pushing the envelope of the size
    problems that can be handled in practice

II. Compilation to a Combinatorial Core
Example Planning
  • Planning find a (partially) ordered set of
    actions that transform a given initial state to a
    specified goal state.
  • in most general case, can cover most forms of
    problem solving
  • scheduling fixes set of actions, need to find
    optimal total ordering
  • planning problems typically highly non-linear,
    require combinatorial search

Some Applications of Planning
  • Autonomous systems
  • Deep Space One Remote Agent (Williams Nayak
  • Mission planning (Muscettola 1998)
  • Natural language understanding
  • TRAINS (Allen 1998) - mixed initiative dialog
  • Software agents
  • Softbots (Etzioni 1994)
  • Goal-driven characters in games (Nareyek 1998)
  • Help systems - plan recognition (Kautz 1989)
  • Manufacturing
  • Supply chain management (Crawford 1998)
  • Software understanding / verification
  • Bug-finding (goal undesired state) (Jackson

State-space Planning
  • State complete truth assignment to a set of
    variables (fluents)
  • Goal partial truth assignment (set of states)
  • Operator a partial function State State
  • specified by three sets of variables
  • precondition, add list, delete list
  • (STRIPS, Fikes Nilsson 1971)

Abdundance of Negative Complexity Results
  • I. Domain-independent planning PSPACE-complete
    or worse
  • (Chapman 1987 Bylander 1991 Backstrom 1993)
  • II. Bounded-length planning NP-complete
  • (Chenoweth 1991 Gupta and Nau 1992)
  • III. Approximate planning NP-complete or worse
  • (Selman 1994)

  • Traditional domain-independent planners can
    generate plans of only a few steps.
  • Most practical systems try to eliminate search
  • Tractable compilation
  • Custom, domain-specific algorithms
  • Scaling remains problematic when state space is
    large or not well understood!

Planning as Satisfiability
  • SAT encodings are designed so that plans
    correspond to satisfying assignments
  • Use recent efficient satisfiability procedures
    (systematic and stochastic) to solve
  • Evaluation performance on benchmark instances

instantiated propositional clauses
axiom schemas
problem description
SAT engine(s)
satisfying model
SAT Encodings
  • Propositional CNF no variables or quantifiers
  • Sets of clauses specified by axiom schemas
  • Use modeling conventions (Kautz Selman 1996)
  • Compile STRIPS operators (Kautz Selman 1999)
  • Discrete time, modeled by integers
  • upper bound on number of time steps
  • predicates indexed by time at which fluent holds
    / action begins
  • each action takes 1 time step
  • many actions may occur at the same step
  • fly(Plane, City1, City2, i) É at(Plane, City2, i

Solution to a Planning Problem
  • A solution is specified by any model (satisfying
    truth assignment) of the conjunction of the
    axioms describing the initial state, goal state,
    and operators
  • Easy to convert back to a STRIPS-style plan

Satisfiability Testing Procedures
  • Systematic, complete procedures
  • Davis-Putnam (DP)
  • backtrack search unit propagation (1961)
  • little progress until 1993 - then explosion of
    improved algorithms implementations
  • satz (1997) - best branching heuristic
  • See SATLIB 1998 / Hoos Stutzle
  • csat, modoc, rel_sat, sato, ...
  • Stochastic, incomplete procedures
  • Walksat (Kautz, Selman Cohen 1993)
  • greedy local search noise to escape local
  • outperforms systematic algorithms on random
    formulas, graph coloring, (DIMACS 1993, 1997)

Walksat Procedure
  • Start with random initial assignment.
  • Pick a random unsatisfied clause.
  • Select and flip a variable from that clause
  • With probability p, pick a random variable.
  • With probability 1-p, pick greedily
  • a variable that minimizes the number of
    unsatisfied clauses
  • Repeat until time limit reached.

Planning Benchmark Test Set
  • Extension of Graphplan benchmark set
  • Graphplan (Blum Furst 1995) - best
    domain-independent state-space planning algorithm
  • logistics - complex, highly-parallel
    transportation domain, ranging up to
  • 14 time slots, unlimited parallelism
  • 2,165 possible actions per time slot
  • optimal solutions containing 150 distinct actions
  • Problems of this size (1018 configurations) not
    previously handled by any state-space planning

Scaling Up Logistics Planning
What SATPLAN Shows
  • General propositional theorem provers can compete
    with state of the art specialized planning
  • New, highly tuned variations of DP surprising
  • result of sharing ideas and code in large SAT/CSP
    research community
  • specialized engines can catch up, but by then new
    general techniques
  • Radically new stochastic approaches to SAT can
    provide very low exponential scaling
  • 2 orders magnitude speedup on hard benchmark
  • Reflects general shift from first-order
    non-standard logics to propositional logic as
    basis of scalable KRR systems

Further Paths to Scale-Up
  • Efficient representations and new SAT engines
    extend the range of domain-independent planning
  • Ways for further improvement
  • Better SAT encodings
  • Better general search algorithms

III. Improved Encodings Compiling Control
Kinds of Control Knowledge
  • About domain itself
  • a truck is only in one location
  • airplanes are always at some airport
  • About good plans
  • do not remove a package from its destination
  • do not unload a package and immediate load it
  • About how to search
  • plan air routes before land routes
  • work on hardest goals first

Expressing Knowledge
  • Such information is traditionally incorporated in
    the planning algorithm itself
  • or in a special programming language
  • Instead use additional declarative axioms
  • (Bacchus 1995 Kautz 1998 Chen, Kautz, Selman
  • Problem instance operator axioms initial and
    goal axioms control axioms
  • Control knowledge constraints on search and
    solution spaces
  • Independent of any search engine strategy

Axiomatic Control Knowledge
  • State Invariant A truck is at only one location
  • at(truck,loc1,i) loc1 ¹ loc2 É Ø
  • Optimality Do not return a package to a location
  • at(pkg,loc,i) Ø at(pkg,loc,i1) iltj É Ø
  • Simplifying Assumption Once a truck is loaded,
    it should immediately move
  • Ø in(pkg,truck,i) in(pkg,truck,i1)
    at(truck,loc,i1) É Ø at(truck,loc,i2)

Adding Control Kx to SATPLAN
Problem Specification Axioms
Control Knowledge Axioms
Instantiated Clauses
As control knowledge increases, Core shrinks!
SAT Simplifier
SAT Core
SAT Engine
Logistics - Control Knowledge
Scale Up with Compiled Control Knowledge
  • Significant scale-up using axiomatic control
  • Same knowledge useful for both systematic and
    local search engines
  • simple DP now scales from 1010 to 1016 states
  • order of magnitude speedup for Walksat
  • Control axioms summarize general features of
    domain / good plans not a detailed program!
  • Obtained benefits using only admissible control
    axioms no loss in solution quality (Cheng,
    Kautz, Selman 1999)
  • Many kinds of control knowledge can be created
  • Machine learning (Minton 1988, Etzioni 1993,
    Weld 1994, Kambhampati 1996)
  • Type inference (Fox Long 1998, Rintanen 1998)
  • Reachability analysis (Kautz Selman 1999)

IV. Improved SAT Solvers Randomized Restarts
  • Combinatorial search methods often exhibit
  • a remarkable variability in performance. It is
  • common to observe significant differences
  • between
  • different heuristics
  • same heuristic on different instances
  • different runs of same heuristic with different
    random seeds

Example SATZ
Preview of Strategy
  • Well put variability / unpredictability to our
    advantage via randomization / averaging.

Cost Distributions
  • Consider distribution of running times of
    backtrack search on a large set of equivalent
    problem instances
  • renumber variables
  • change random seed used to break ties
  • Observation (Gomes 1997) distributions often
    have heavy tails
  • infinite variance
  • mean increases without limit
  • probability of long runs decays by power law
    (Pareto-Levy), rather than exponentially (Normal)

(No Transcript)
Heavy-Tailed Distributions
  • infinite variance infinite mean
  • Introduced by Pareto in the 1920s
  • probabilistic curiosity
  • Mandelbrot established the use of heavy-tailed
    distributions to model real-world fractal
  • Examples stock-market, earth-quakes, weather,...

How to Check for Heavy Tails?
  • Log-Log plot of tail of distribution
  • should be approximately linear.
  • Slope gives value of
  • infinite mean and
    infinite variance
  • infinite variance

(No Transcript)
Heavy Tails
  • Bad scaling of systematic solvers can be caused
    by heavy tailed distributions
  • Deterministic algorithms get stuck on particular
  • but that same instance might be easy for a
    different deterministic algorithm!
  • Expected (mean) solution time increases without
    limit over large distributions

Randomized Restarts
  • Solution randomize the systematic solver
  • Add noise to the heuristic branching (variable
    choice) function
  • Cutoff and restart search after a fixed number of
  • Provably Eliminates heavy tails
  • In practice rapid restarts with low cutoff can
    dramatically improve performance
  • (Gomes 1996, Gomes, Kautz, and Selman 1997,

Rapid Restart on LOG.D
Note Log Scale Exponential speedup!
Increased Predictability
  • Overall insight
  • Randomized tie-breaking with
  • rapid restarts can boost
  • systematic search
  • Related analysis Luby Zuckerman 1993 Alt
    Karp 1996.
  • Other applications sports scheduling, circuit
    synthesis, quasigroup competion,

  • Discussed approaches to scalable KRR systems
    based on propositional reasoning and search
  • Shift to 10,000 variables and 106 clauses has
  • opened up new applications
  • Methodology
  • Model as SAT
  • Compile away as much complexity as possible
  • Use off-the-shelf SAT Solver for remaining core
  • Analogous to LP approaches

Conclusions, cont.
  • Example AI planning / SATPLAN system
  • Order of magnitude improvement (last
  • 10 step to 200 step optimal plans
  • Huge economic impact possible with 2 more!
  • up to 20,000 steps ...
  • Discussed themes in Encodings Solvers
  • Local search
  • Control knowledge
  • Heavy-tails / Randomized restarts

Tractable Knowledge Compilation Summary
  • Many techniques have been developed for compiling
    general KR languages to computationally tractable
  • Horn approximations (Kautz Selman 1993, Cadoli
    1994, Papadimitriou 1994)
  • Model-based representations (Kautz Selman 1992,
    Dechter Pearl 1992, Roth Khardon 1996,
    Mannila 1999, Eiter 1999)
  • Prime Implicates (Reiter DeKleer 1987, del Val
    1995, Marquis 1996, Williams 1998)

Limits to Compilability
  • While practical for some domains, there are
    fundamental theoretical limitations to the
  • some KBs cannot be compiled into a tractable
    form unless polynomial hierarchy collapses
  • Sometimes must face NP-hard reasoning problems
    head on
  • will describe how advances in modeling and SAT
    solvers are pushing the envelope of the size
    problems that can be handled in practice

Logistics Increased Predictability
Example SATZ