What do you mean What do I mean

What do you mean, “What do I mean? ” Lecture 8 -2 November 18 th, 1999 CS 250 Lecture 8 -2 CS 250: Intro to AI/Lisp

Project comments • Need copy-editing • Style – Page numbers, citations • Code printouts – No crazy wrapping • Philosophy projects Lecture 8 -2 CS 250: Intro to AI/Lisp

Ontolingua • Stanford ontology server – Suite of ontology authoring tools – Library of modular reusable ontologies Lecture 8 -2 CS 250: Intro to AI/Lisp

How do we build an ontology? • Knowledge engineering • Knowledge acquisition • Ontological engineering Lecture 8 -2 CS 250: Intro to AI/Lisp

Representing U of C • What kinds of questions might we want to ask of our knowledge base? Lecture 8 -2 CS 250: Intro to AI/Lisp

Steps in Building • • • Decide what to talk about Decide on a vocabulary Encode general rules Encode an instance Pose queries Lecture 8 -2 CS 250: Intro to AI/Lisp

What do we get from logic? • Logics consist of: – Syntax – Semantics – Proof theory • Expressive, but doesn’t say what to express Lecture 8 -2 CS 250: Intro to AI/Lisp

A Few Terms • Knowledge engineering - Art & science of transforming worldly knowledge into computer reasonable form • Knowledge acquisition - Squeezing knowledge from the heads of experts Lecture 8 -2 CS 250: Intro to AI/Lisp

Declarative Approach Rides Again • Write down what you know, and let the system figure out the rest • Separate inferencing from representation – Design an inferencing engine that works with many representations – Free to focus on the best representation Lecture 8 -2 CS 250: Intro to AI/Lisp

Good Qualities for a Knowledge Base • • • Clarity Coherence Extensibility Avoid favoring encodings Minimal ontological commitment From “Toward Principles for the Design of Ontologies Used for Knowledge Sharing” Lecture 8 -2 CS 250: Intro to AI/Lisp

KE Questions • For every sentence added to the knowledge base: – Why is this true? Can its truth be decomposed? – Is it widely applicable? Can I broaden this observation? – Do I need a predicate to denote this class of objects? How does the class relate to other classes? Subclasses? Other class properties? Lecture 8 -2 CS 250: Intro to AI/Lisp

KE Strategy Decide what to talk about – What to focus on, what to ignore Vocabulary of predicates, functions & constants Encode general domain knowledge Encode a specific problem instance Sit back and ask questions Lecture 8 -2 CS 250: Intro to AI/Lisp

1 -Bit Adder 1 2 1 3 2 Lecture 8 -2 CS 250: Intro to AI/Lisp

What are We Talking About • Some concepts we’ll need – Wires as connectors – Gates (AND, OR, XOR & NOT) – Inputs – Outputs • What don’t we need? Latency, layout, CMOS, time Lecture 8 -2 CS 250: Intro to AI/Lisp

Representing Stuff • Distinguish gates from one another – Constants • Gate types – Type functions > Type(X 1) = XOR • Terminals – Output terminal function: Out(1, X 1) • Connectivity Lecture 8 -2 CS 250: Intro to AI/Lisp

Encode General Rules • If two terminals are connected, they have the same signal t 1, t 2 Connected(t 1, t 2) Signal(t 1)=Signal(t 2) • The signal at every terminal is either on or off (but not both) t Signal(t)=On Signal(t)=Off On Off • An XOR gate is on iff its inputs are different g Type(g)=XOR Signal(Out(1, g)=On Signal(In(1, g)) Signal(In(2, g)) Lecture 8 -2 CS 250: Intro to AI/Lisp

Encode Specific Instance • Encode the circuit – Gate info – Connections among gates Lecture 8 -2 CS 250: Intro to AI/Lisp

Ask the $64, 000 Question • When will the first output of C 1 be off and the second output of C 1 to be on? • Is the circuit correct? – What are the possible sets of values of all the terminals for the adder circuit? i 1, i 2, o 1, o 2 Signal(In(1, C 1))=i 1 Signal(In(2, C 1))=i 2 Signal(In(3, C 1))=i 3 Signal(Out(1, C 1))=o 1 Signal(Out(2, C 1))=o 2 Lecture 8 -2 CS 250: Intro to AI/Lisp

Other KR’s • Case-based reasoning • Bayesian networks • Neural networks Lecture 8 -2 CS 250: Intro to AI/Lisp

Representational Adequacy • Metaphysical adequacy Could the world have the representational form suggested without a contradicting the facts of the aspect of the reality we’re interested in? • Epistemological adequacy Express facts about the world in a practical way • Heuristic adequacy Are the reasoning processes used in solving a problem expressible? Lecture 8 -2 CS 250: Intro to AI/Lisp

General Ontologies • • Categories Measures Composite Objects Time, Space and Change Events and Processes Physical Objects Substances Mental Objects and Beliefs Lecture 8 -2 CS 250: Intro to AI/Lisp

Categories • Reification – How many people live on Earth? • Inheritance • Creating taxonomies – Kentucky Fried Chicken – Dewey decimal – Lo. C – Me. Sh Lecture 8 -2 CS 250: Intro to AI/Lisp

Measures • Examples: Height, mass, cost • Measure = Units function + a Number Lecture 8 -2 CS 250: Intro to AI/Lisp

Composite Objects • Not inheritance – Difference between subclass and member • Schema • Script Lecture 8 -2 CS 250: Intro to AI/Lisp

Composite Objects • Not inheritance – Difference between subclass and member • General event descriptions – Schema – Script Lecture 8 -2 CS 250: Intro to AI/Lisp

Using Events to Represent Change • What’s the problem? – Continuous time – Multiple agents – Actions of different durations • Event calculus - Reify events Lecture 8 -2 CS 250: Intro to AI/Lisp

Event Calculus Vocabulary • Events are splotches in the space-time continuum • Events have subevents • Some events are intervals Lecture 8 -2 CS 250: Intro to AI/Lisp

Examples • Suppose we wish to represent facts about market manias f f Bulb. Eating Sub. Event(f, Tulip. Mania) Part. Of(Location(f), Holland) s s Stock. Frenzy Sub. Event(s, USBull. Market) Part. Of(Location(f), ? ? ) s s Stock. Frenzy Sub. Event(s, USBull. Market) Traded. On(Exchange(s), NASDAQ) Lecture 8 -2 CS 250: Intro to AI/Lisp

Place • How are places like intervals? • Relation In holds among places • Location function: Maps an object to the smallest place that contains it Lecture 8 -2 CS 250: Intro to AI/Lisp

Kurt D. Fenstermacher: Sonnenfeld directed: Men in Black (1997) Get Shorty (1995) The Addams Family (1991) Processes • Why do we need processes when we have events? • How can we say: – Barry Sonnenfeld was flying some time yesterday E(Flying(Barry), Yesterday) – Barry was flying all day yesterday T(Flying(Barry), Yesterday) Lecture 8 -2 CS 250: Intro to AI/Lisp

A Logical Blender Suppose Bill is accused of killing a zucchini, and when the cold, but efficient, Detective Frigerator (known to his pals as simply “Re”) questions the orange juice pitcher in FOPL, the orange juice has no idea how to say: “Bill was in the kitchen with the tomato all day yesterday” Lecture 8 -2 CS 250: Intro to AI/Lisp

Composite Events • Use And to combine two events with the usual semantics: p, q, e T(And(p, q), e) T(p, e) T(q, e) And isn’t so bad, but disjunction is a bit more complicated -- how do we say: “I saw the whole thing, the beef or the broccoli stabbed the zucchini all afternoon. ” Lecture 8 -2 CS 250: Intro to AI/Lisp

Time & Intervals • Time is pretty important – Divvy up time into: Moments and Extended. Intervals – Define a couple handy functions Start End Time Date Lecture 8 -2 CS 250: Intro to AI/Lisp

When Intervals Get Together • • • Meet Before After During Overlap Lecture 8 -2 CS 250: Intro to AI/Lisp

Objects in the Space-Time Continuum • Remember that events are splotches of space-time • Some events have coherence through time • Need to capture the idea of an object existing through time Lecture 8 -2 CS 250: Intro to AI/Lisp

Roman Empire • Roman Empire spread across much of Eurasia, expanding and contracting, from 753 B. C. until the 5 th century A. D. Lecture 8 -2 CS 250: Intro to AI/Lisp

Roman Empire at 218 B. C. Lecture 8 -2 CS 250: Intro to AI/Lisp

Roman Empire at 117 A. D. Lecture 8 -2 CS 250: Intro to AI/Lisp

Roman Empire at 395 A. D. Lecture 8 -2 CS 250: Intro to AI/Lisp

Fluents • Roman Empire is an event – Subevents include • First, Second and Third Punic Wars • One of the first known hammer and anvil movements in battle (216 BC @ Cannae) • A fluent allows us to capture the notion of the Roman Empire throughout time T(In(Gaul, Roman Empire), AD 12) T(Male(Emperor(Roman. Empire)), 1 st. Century. AD) Lecture 8 -2 CS 250: Intro to AI/Lisp

Fluent Flavors • Fluent is a function, f: Situations Fvalues – Domain is the set of all situations (states of the world) If Fvalues is {TRUE, FALSE} then it’s a Propositional fluent If Fvalues is {All situations} then it’s a Situational fluent Lecture 8 -2 CS 250: Intro to AI/Lisp

Substances • Less vs. fewer • Intrinsic vs. extrinsic properties • Substances are those things that are fungible Lecture 8 -2 CS 250: Intro to AI/Lisp

Going, Like, Totally Mental • What are other agents know, and what are they thinking? – “Everybody’s looking at me” – “They’re trying to kill me” – “You look like someone who knows where I can find extra virgin olive oil” • Start with a Believes predicate Believes(Agent, x) Lecture 8 -2 CS 250: Intro to AI/Lisp

Reification & You • A good first pass: Believes(Agent, Flies(Superman)) • Treat Flies(Superman) as a propositional fluent – Relationships like Believes, Know and When between agents and propositions are propositional attitudes • The problem: Can Clark fly? Lecture 8 -2 CS 250: Intro to AI/Lisp

“It is clear. ” • Referential transparency – Any term can be substituted for an equal term – FOL is referentially transparent Lecture 8 -2 CS 250: Intro to AI/Lisp

Knowing for Action • Knowing preconditions: What do you need to know to do action a? • Knowledge effects: What effect does performing action a have on an agent’s knowledge? Lecture 8 -2 CS 250: Intro to AI/Lisp

Replacing that Zucchini • Grocery shopping – Percepts – Actions – Goals – Environment Lecture 8 -2 CS 250: Intro to AI/Lisp