Representation Knowledge Knowledge engineering: principles and pitfalls Ontologies Examples
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Knowledge Engineer Populates KB with facts and relations Must study and understand domain to pick important objects and relationships Main steps: Decide what to talk about Decide on vocabulary of predicates, functions & constants Encode general knowledge about domain Encode description of specific problem instance Pose queries to inference procedure and get answers
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Knowledge EngineerRunway Safety/Runway Incursion
Decide what to talk about
Airport, planes, risks, mitigations
Decide on vocabulary of predicates, functions & constants Encode general knowledge about domain
Circles and Arrows Taxonomy in PowerLoom Rules describe the inferential knowledge "If the pilot is much more senior than the copilot, there is a risk that the copilot will be intimidated." "If the plane is fully loaded with fuel, its takeoff distance is increased." Modeling the Tenerife Disaster
Encode description of specific problem instance
Pose queries to inference procedure and get answers
"Real time" play of events to get: {(risk1 (mit11..mit1n)) (risk2 (mit21..mit2m))...} How do you sort the risks and sort the mitigations with the risk?????
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Knowledge engineering vs. programming
1. 2. 3. 4.
Knowledge Engineering Choosing a logic Building knowledge base Implementing proof theory Inferring new facts
Programming Choosing programming language Writing program Choosing/writing compiler Running program
Why knowledge engineering rather than programming? Less (Different?) work: just specify objects and relationships known to be true, but leave it to the inference engine to figure out how to solve a problem using the known facts. [Capture the Domain]
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Properties of good knowledge bases Expressive What can you say? Concise How hard is it to say something? Contextinsensitive Can you represent many domains? Unambiguous Effective Clear Correct ...
Tradeoffs: e.g., sacrifice some correctness if it enhances brevity. PowerLoom, Prolog, Owl, RDF all offer different features (Mostly speed v. completeness v. expressivity)
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Efficiency Ideally: Not the knowledge engineer's problem To me efficiency is the wrong question. Can you make anything work at all? Can you capture all the "knowledge" in a domain so that you can compute with it? Can the solution be easily maintained? Can the solution be used in planning as well as diagnosis?
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Pitfall: design KB for human readers
KB should be designed primarily for inference procedure! e.g.,VeryLongName predicates:
BearOfVerySmallBrain(Pooh) does not allow inference procedure to infer that Pooh is a bear, an animal, or that he has a very small brain, ... Rather, use:
In other words: BearOfVerySmallBrain(pooh) = x(pooh)
Bear(Pooh) b, Bear(b) Animal(b) a, Animal(a) PhysicalThing(a) ... [See AIMA ch. 10 for many examples]
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Debugging
In principle, easier than debugging a program, because we can look at each logic sentence in isolation and tell whether it is correct. [Beware of subtle interactions, but code has this problem as well.] Example: x, Animal(x) b, BrainOf(x) = b
means "there is some object that is the value of the BrainOf function applied to an animal" and can be corrected to mean "every animal has a brain" without looking at other sentences.
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Ontology Collection of concepts and interrelationships Widely used in the database community to "translate" queries and concepts from one database to another, so that multiple databases can be used conjointly (database federation)
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Ontology Example
Khan & McLeod, 2000
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A Government Ontology
State
hasP hasB
People Sovereign State Nation State Anarchy
hasRules
for
Boundary Sovereignty
via
hasS
hasS
Political Sovereignty
via
Economic Sovereignty
via
System Religious System
No Rules
People
Political System
hasRules of by for
Economic System
Free Market
implements
Socialism
implements
Marxism
Oligarchy
Democracy
of by for
Monarchy
for of by Royal Family
of by Small Group
Capitalism (Adam Smith)
Hybrid Economy
Theocracy
of by for
People
Constitutional Democracy
hasRules
"Good" Communism
for
Religious Leaders
People
Totalitarian
People "Bad" Communism
Oligarchy
for of by
of by
Law Family
of by for
Dictatorship
of by for
Political Leaders
of
by
for
Parents
Family Members
Dictator
Communist Party
For details about the United States instances, see last page.
Russia Under Lenin was a Nation State whose Political and Economic Sovereignty was via Good Communism Russia Under Stalin was a Nation State whose Political and Economic Sovereignty was via Bad Communism
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Towards a general ontology Develop good representations for: categories measures composite objects time, space and change events and processes physical objects substances mental objects and beliefs ...
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Representing Categories (or Concepts)
We interact with individual objects, but... much of reasoning takes place at the level of categories. Representing categories in FOL: use unary predicates in a table form (small set of objects) e.g., Tomato(x)
based on its properties
reification: turn a predicate or function into an object e.g., use constant symbol Tomatoes to refer to set of all tomatoes "x is a tomato" expressed as "xTomatoes" Strong property of reification: can make assertions about reified category itself rather than its members e.g., Population(Humans) = 5e9 Allows us to easily reason about categories.
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Categories: Allow inheritance
to organize and simplify knowledge base e.g., if all members of category Food are edible and Fruits is a subclass of Food and Apples is a subclass of Fruits then we know (through inheritance) that apples are edible. Taxonomy: hierarchy of subclasses Because categories are sets, we handle them as such. e.g., two categories are disjoint if they have no member in common a disjoint exhaustive decomposition is called a partition etc...
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Example: Taxonomy of hand/arm movements
Hand/arm movement Gestures Manipulative Acts Mimetic Deictic Communicative Symbols Referential Modalizing Unintentional Movements
Quek,1994, 1995.
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Classification
In FOL all facts and rules are simply thrown into the mix. No organization. In taxonomic hierarchies there is obviously some organization. Classification, a type of inference, is possible. Watch the classification movie. http://www.isi.edu/isd/LOOM/loomclassifier.mov
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Measures
Can be represented using units functions e.g., Length(L1) = Inches(1.5) = Centimeters(3.81) Measures can be used to describe objects e.g., Mass(Tomato12) = Kilograms(0.16)
Caution: be careful to distinguish between measures and objects e.g., b, bDollarBills CashValue(b) = $(1.00)
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Composite Objects
One object can be part of another.
PartOf relation is transitive and reflexive: e.g., PartOf(Bucharest, Romania) PartOf(Romania, EasternEurope) PartOf(EasternEurope, Europe) Then we can infer Part Of(Bucharest, Europe) [An example of a backward chaining rule]
Composite object: any object that has parts
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Composite Objects (cont.)
Categories of composite objects often characterized by their structure, i.e., what the parts are and how they relate. e.g., a Biped(a) ll, lr, b Leg(ll) Leg(lr) Body(b) PartOf(ll, a) PartOf(lr, a) PartOf(b, a) Attached(ll, b) Attached(lr, b) ll lr x Leg(x) PartOf(x, a) (x = ll x = lr) [Problems: How about a onelegged biped? Such description can be used to describe any objects, including events. We then talk about schemas and scripts.
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Events
Chunks of spatiotemporal universe (space and time) [see. p. 336]
e.g., consider the event WorldWarII. It took place in some "place" during some "time" it has parts or subevents: SubEvent(BattleOfBritain, WorldWarII) it can be a subevent: SubEvent(WorldWarII, TwentiethCentury) Intervals: events that include as subevents all events occurring in a given time period (thus they are temporal sections of the entire spatial universe). Cf. situation calculus: fact true in particular situation event calculus: event occurs during particular interval
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Events (cont.)
Places: spatial sections of the spatiotemporal universe that extend through time Use the predicate In(x) to denote subevent relation that holds when one event's spatial projection is part of another's: e.g. In(NewYork, USA)
Location function: maps an object to the smallest place that contains it: x,l Location(x) = l At(x, l) ll At(x, ll) In(l, ll)
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Times, Intervals and Actions
Time intervals can be partitioned between moments (=zero duration) and extended intervals: Absolute times can then be derived from defining a time scale (e.g., seconds since midnight GMT on Jan 1, 1900) and associating points on that scale with events. The functions Start and End then pick the earliest and lates...