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Ontologies of Information Structure and
Commonsense Psychology
Jerry R. Hobbs
USC/ISI
Marina del Rey, CA
Information Structure
Motivation
The best answer to a question is often a diagram, a graph, a map, a photograph, a video.
What is the Krebs cycle?
How has the average height of adult American males varied over the years?
How did the Native Americans get to America?
What does Silvio Berlusconi look like?
What happened on September 11, 2001?
Grounding Symbols in Cognition
cause(perceive(a,x), cognize(a,c))
objectstateevent
processabsence
...
concept(including
propositions)
Grounding Symbols in Cognition
cause(perceive(a,smoke), cognize(a,fire))cause(perceive(a,cloud), cognize(a,dog))cause(perceive(a,bell), cognize(a,food))
No necessary causal connection between x and c
This schema makes symbols possible
cause(perceive(a,x), cognize(a,concept-of(x)))
but other things as well
Grounding Symbols in Cognition
cause(present(b,x,a), perceive(a,x))
cause(perceive(a,x), cognize(a,c))
cause(car beeps, driver hears beep)
cause(driver hears beep, driver remembers seat belt)
Intention and Convention in Communication
Unintentional: fidget --> nervous “ouch” --> pain
Presenter intends concept, but not recognition of intent: my door is closed --> I’m not in
Recognizing Intent
know(b, cause(present(b,x,a), cognize(a,c)))
goal(b,cognize(a,c))
goal(b,g1) & know(b, cause(g2,g1)) & etc --> goal(b,g2)
So b has goal present(b,x,a), an executable action, so he does it.
a looks for causal explanation of present(b,x,a)and comes up with exactly this
Intention is recognized.
Gricean Nonnatural Meaning
If an agent b has a goal g1 and g2 tends to cause g1, then b may have as a goal that g2 cause g1.
If an agent b has as a goal that g2 cause g1, then b has the goal g2.
When a recognizes this plan, he will recognize not only b’s goal to have a cognize c, but also b’s intention that a do so by virtue of the causal relation between b’s presenting x and a’s cognizing c.
Mutual Belief and Convention
Mutual Belief:
mb(s,p) & member(a,s) --> believe(a,p) mb(s,p) --> mb(s,mb(s,p))
Structure of a communicative convention:
mb(s, cause(present(b,x,a), cognize(a,c))) where member(a,s) and member(b,s)
e.g. red flag with white diagonal in community of boaters means “diver below”
represent(x,c,s)
symbol content
Composition in Symbol Systems
Symbol System Content Domaincomposite
symbol
basicconcept/
proposition
atomicsymbol
complexconcept/
proposition
compositionoperation
compositionoperation
interpretation
interpretation
Speech and Text(within sentences)
Symbol System Content Domainsentences
“a man works”
basicpropositions
man(x), work(y)
words“a” “man”, “works”
complexproposition
man(x) & work(x)
pred-arg rels,conjunction
concatenation
interpretation
interpretation
Speech and Text(Discourse)
Symbol System Content Domain
discourse
sentencemeanings
sentences
augmenteddiscoursemeaning
coherence relations:causality, similarity,
figure-ground
concatenation
interpretation
interpretation
Tables
R
a b
Spatial arrangement ==> predicate-argument relations
R(a,b)
Beeps in Car
Atomic symbol: beep ==> something’s wrong
Composite symbols: beep ..... beep ..... beep ..... ==> fasten seat belt If car is running: beep beep beep beep ==> door is still open If car is off: beep beep beep beep ==> lights are still on
Maps
Underlying regions of single color/pattern
icons
internal structureof icons
labels
name of entity
names
entities
meaningfulregion
overlay iconson field
icon and labeladjacent
categoriesof entities
locationof entities
==>
==>
==>
==>
==>
==>
Process Diagrams
icons entities
adjacentgrouped icons
states
adjacent groupsw arrows between
statetransitions
(Futrelle, 1999)
Documents (Scott & Powers, 2003)
Title
Body
Adjacent Paragraphs(mod Page, Col breaks)
Diagram neardescription
Conveys content of body
Main detailed content
Read sequentially
Coreference
Similarly, Web pages, PowerPoint presentations, ...
Face-to-Face Conversation
Atomic elements:
Speech, prosody
Facial expression
Gaze direction
Body position
Gestures w hands and arms
Composition operators:
Temporal adjacency
Temporal synchrony
Need to determinemeaning/functionof various behaviors
Larger-Scale CommunicativePerformances
Lectures w PowerPoint slides
Plays
Demos
....
Coreference
Two noun phrases
Icon and label
Same icon in two state groups in diagram
Region of photo and noun phrase in caption and phrase in text
Iconic gesture and phrase in speech
Useful for image search by keywords
Modalities and Media (Hovy & Arens, 1990; Allwood, 2002)
Channels of perception: optical, acoustic, chemical, pressure, temperature, ...
Greatest opportunities for composition
Communication devices:
Primary: speech, gesture Secondary: writing, drawing, telephones, videotape, computer terminals, ....
Advantages and disadvantages of each e.g., visual: exploit 2-D structure to convey relations
Manifestations of Symbolic Entities
We group together classes of symbolic entities sharing same content and call them first class entities.
manifest(x1,x) & represent(x,c,s) --> represent(x1,c,s)
(defeasibly -- if P is in content of x then defeasibly it is in content of x1)
Hamletthe play
The performanceof Hamlet
A particularperformance
of Hamlet
A videotapeof that
performance
A copyof that
videotape
The text of Hamlet
An editionof Hamlet
A copy ofthat edition
(Pease & Niles, 2001)
CommonsensePsychology
(work with Andrew Gordon, USC/ICT)
Methodology
Agents plan, so to discover what agents know, investigate strategies.
Picked 10 planning domains: politics, warfare, personal relationships, artistic performance, sales, immunology, animal camoflage, ...
Interviewed experts to learn strategies
Resulted in 372 strategies
Rewrote strategies in controlled vocabulary -- 988 terms
Classified terms into 48 representational areas (space, time, ...); 18 general knowledge; 30 commonsense psychology
Enrich each representational area by text mining
Formalize
(Gordon, 2000)
Methodology
Agents plan, so to discover what agents know, investigate strategies.
Picked 10 planning domains: politics, warfare, personal relationships, artistic performance, sales, immunology, animal camoflage, ...
Interviewed experts to learn strategies
Resulted in 372 strategies
Rewrote strategies in controlled vocabulary -- 988 terms
Classified terms into 48 representational areas (space, time, ...); 18 general knowledge; 30 commonsense psychology
Enrich each representational area by text mining
Formalize
(Gordon, 2000)
Machiavelli Sun Tzu his wife
Theories So Far
Memory
Knowledge Management
Envisioning (Thinking)
Goals and Planning
Why is Memory Important?
We plan to remember actions/information at the appropriate time.
We are responsible for remembering. Why was Mary angry that John forgot her birthday? But forgetting is often a less serious breach than some other reason. Why didn’t you get me a present? I forgot it was your birthday. vs. I didn’t want to.
Naive Model of Memory
Focus of Attention
Memory
concept
concept
store
retrieve
If in memory,then it was stored
Accessibility
concept-1
concept-2
concept-3
concept-4
Concepts in memoryhave varying accessibility.
threshold
Concepts notretrievable
Associations and Accessibility
concept-1
concept-2
concept-3
concept-4
concept-1
concept-2
concept-3
concept-4
concept-0
Associations and Accessability
Thinking of concepts makes associated concepts more accessible.
This give agents partial control over memory retrieval.
Technique of memorization: Rich associations.
“Remember” and “Forget”
in memory above accessibility threshold --> remember
retrieve --> remember
cause self to retrieve --> remember
cause self to retrieve after some effort --> remember
forget concept <--> concept drops below accessibility threshold
Remembering for a Time
We store concepts in memory until we need them and then forget them.
Where did I park my car today? vs. Where did I park my car on January 4?
We use memory to satisfy knowledge prerequisites for planned actions.
Knowledge Management:Belief
Reify agents and propositions: believe(a,p)
Reasoning is possible inside belief: believe(a,p) & believe(a,p-->q) & etc --> believe(a,q)
Perception causes belief (seeing is believing)
Communication tends to cause belief
BDI: We act in ways that maximize satisfaction of our goals, given our beliefs
Graded Belief (Friedman & Halpern, 2001)
0 gb(a,p) 1
gb(a, p&q) min(gb(a,p), gb(a,q))
gb(a, p & [p-->q]) = gb(a,q)
gb(a,p) = 1 <--> believe(a,p)
The higher the graded belief, the more likely agent is to act on it
Knowledge Domain
Sentence = set of propositions + a claim
king(x,France) & bald(x)
Knowledge domain: Has a set of characteristic predicates Is a set of sentences all of whose claims have predicates that are in the characteristic set
Expert: Agent is defeasibly an expert in a knowledge domain if agent knows sampling of facts in the knowledge domain (tests, inference from displays of knowledge)
propositionalcontent claim
Mutual Belief
mb(s,p) & member(a,s) --> believe(a,p)
mb(s,p) --> mb(s,mb(s,p))
These rules are mutually believed
Can show that if a knows b is a member of s and a knows s mutually believes p, then a knows that b believes p
Inference of who knows what / who is an expert in what from membership in communities
Causal Complex
e1 e2
e3 e4e
....
s
causal-complex(s,e)
e1 s, ....
When every event or state in s happens or holds, then e happens or holds.
All eventualities in s are relevant.
causally-involved(ei,e)
causal complex
effect
Cause
In a causal complex, some eventualities are distinguished as causes.
power on
finger insocket
shock
What is presumable depends on task, context, knowledge base, ....
presumable
cause
Causes are the focus ofplanning, prediction,
explanation, interpretingdiscourse
(but not diagnosis)
Envisioning (Thinking)
e1
e2
e3
e4
e5
e6
e7
e8
e11
e10
e9
e’s are causally involved
Causal System
Envisioning
e4
e5
e6
e4
e5
e6
e7
e8
e1
e2
e3
e4
e5
e6
Contiguouscausal systems
Envisioning
e1
e2
e3
e4
e5
e6
e7
e8
e11
e10
e9
envisionedcausal system
slice
Agent has this in focus
Envisioned Causal System
e1
e2
e3
e4
e5
e6
e7
e8
e11
e10
e9
ExplanationPrediction
A sequence of envisioned contiguous causal systems
Correspondence with Reality
If the events and states in the ECS are believed, the ECS is the “current world understanding”
Need an account of how graded belief is increased or decreased as predictions and explanations are verified or falsified.
Goals and Planning
Causal Knowledge:
(e1,x)[p’(e1,x) --> (e2)[q’(e2,x) & cause(e1,e2)]] or, p causes q
(e1,x)[p’(e1,x) --> (e2)[q’(e2,x) & cause(e1,e2)]] or, p enables q
where enable(e1,e2) <--> cause(~e1,~e2)
Planning Axioms:
(a,e1,e2)[goal(a,e2) & cause(e1,e2) & etc --> goal(a,e1)]
(a,e1,e2)[goal(a,e2) & enable(e1,e2) --> goal(a,e1)]
subgoal(a,e1,e2)
Goals and Planning
Goals can be ...
competitive adversarial auxilliary .....
Collective Goals
Groups can have goals:
All agents in group mutually believe the group has the goal
All agents have the individual goal that the group achieves its goal
Must bottom out in individual agents’ actions
Organizations are such collective plans made concrete; an agent’s role in an organization is the actions the agent carries out as a subgoal in the collective plan
Where Do Goals Come From?
A False Mystery
Stipulate: goal(a, thrive(a))
All else is causal knowledge/beliefs about what causes thriving
Goal Themes
goal-theme(s,t) <--> ( a,e) [member(a,s) & member(e,t) & etc ---> goal(a,e)]
From group membership, we can infer beliefs and goals and thus behavior (defeasibly)e.g., he’s a puritan / hedonist / geek / ....
group ofagents
set of possibleeventualities
Summary
Ontologies are important for communicating the contents and capabilities of Web sites and Web resources
Most of this information is now in the form of natural language
We need ontologies that are capable of expressing the full range of content in Web sites, forming the basis of a deeper lexical semantics
I have presented first cuts at some of the most basic ontologies needed: services, events, time, space, information, human psychology