Download Common Sense and Artificial Intelliegence

Common Sense
and
Artificial Intelligence
Pradyumna Kumar Reddy
Jayanth Tadinada
Prithvi Raj Kanakam
Satish Kumar Guguloth
Devashish Sethia
It is way past 2001: Where the hell is HAL?
The Goals of Artificial Intelligence
• The need to reconsider the goals of AI
• Mental Amplification
• Thanks to engineering, we can travel faster and
farther than our muscles can take us, see things we
can’t otherwise see, talk louder than our lungs can
shout.
Expert Systems
• Our system: Some diagnosis expert system like
MYCIN
• The Patient: An old rusted car in the back yard
• Question & Answer session with the expert system
 Are there spots on the body? YES
 Are there more spots on the trunk than
anywhere else? YES
What color are the spots? REDDISH BROWN
• Diagnosis: The car has measles
• Degree of confidence: HIGH
Example taken from Google Techtalk by Doug Lenat, may 2006
Expert Systems
(cont.)
• Our system: An intelligent car loan approval system
• Question & Answer session with the expert system
 Date of Birth: 1989
 Time spent at current job: 19 YEARS
• Result: Loan approved
Example taken from Google Techtalk by Doug Lenat, May 2006
Expert Systems
(cont.)
• So why do the “expert” systems have this problem?
• Because they don’t have common sense
• The expert systems only know equations and
variables.
Search
• Is the Eiffel tower taller than the Taj Mahal?
• Cannot combine knowledge it already has access to.
• Why can’t the search engine do the simple math
and give us the answer
• Lack of common sense
Natural Language Processing
•
•
The police watched demonstrators...
…because they feared violence.
…because they advocated violence.
Mary and Sue are sisters.
Mary and Sue are mothers.
• George Burns: “My aunt is in the hospital, I went
to see her today, and took her flowers.”
Gracie Allen: “George, That’s terrible! You should
have brought her flowers.”
Example taken from Google Techtalk by Doug Lenat, may 2006
ASSUME OUR
COMPUTER
NOW HAS
Common Sense
Search
Query: “someone smiling”
When you are happy, you smile
You become happy when someone
you love accomplishes a milestone
Taking one’s first step is a milestone
Parents love their children
Caption: “A mother helping her child
take her first step”
Search
Query: “Government buildings damaged in terrorist
events in Beirut between 1990 and 2001.”
Beirut is in Lebanon
Embassies are govt. buildings
1993 is in the 1990’s
If there was a pipe bombing, then it is mostly
a terrorist attack and not an accident etc.
Document: “1993 pipe bombing of France’s embassy
in Lebanon”
Example taken from Google Techtalk by Doug Lenat, may 2006
Natural Language Processing
•
•
The police watched demonstrators...
…because they feared violence.
…because they advocated violence.
Mary and Sue are sisters.
Mary and Sue are mothers.
• George Burns: “My aunt is in the hospital, I went
to see her today, and took her flowers.”
Gracie Allen: “George, That’s terrible! You should
have brought her flowers.”
Example taken from Google Techtalk by Doug Lenat, may 2006
So How do we
implement
Common Sense?
What is this “Knowledge”?
• Millions of facts, rules of thumb etc.
• Represented as sentences in some language.
• If the language is Logic, then computers can do
deductive reasoning automatically.
• This representation of a set of concepts within a
domain and the relationships between those
concepts is called Ontology
• The sentences are expressed in formal logic
notation.
• The words and the logic sentences about them are
called Formal Ontology
Hierarchy in Ontology
Predicate Calculus Representation
Parents love their children
This can be represented as
(ForAll ?P (ForAll ?C
(implies
(and
(isa ?P Person)
(child ?P ?C))
(loves ?P ?C)))))
For all P, For all C, P is a person AND C is a child of P
implies P loves C
Reasoning Using Logic
Examples:
Simple:
(isa Socrates Man)
(ForAll ?x (implies (isa ?x Man) (isa ?x Mortal)))
(isa Socrates Mortal) =>Yes
Harder:
Using general and specific knowledge
Can a can can-can? => No
Cyc
• Cyc is an AI project that attempts to assemble a
comprehensive ontology and knowledge of everyday
common sense knowledge.
• Its goal is to enable AI applications to perform
human like reasoning.
• The project was started by CYcorp, a Texas based
company.
• All the aforementioned features were incorporated in
Cyc.
Cyc
• Cyc has a huge knowledge base which it uses for
reasoning.
• Contains
• 15,000
predicates
• 300,000
concepts
• 3,200,000 assertions
• All these predicates, concepts and assertions are
arranged in numerous ontologies.
Cyc: Features
Uncertain Results
• Query: “who had the motive for the assassination of
Rafik Hariri?”
• Since the case is still an unsolved political mystery,
there is no way we can ever get the answer.
• In cases like these Cyc returns the various view
points, quoting the sources from which it built its
inferences.
• For the above query, it gives two view points
• “USA and Israel” as quoted from a editorial in Al Jazeera
• “Syria” as quoted from a news report from CNN
Example taken from Google Techtalk by Doug Lenat, may 2006
Cyc: Features
(cont.)
• It uses Google as the search engine in the
background.
• It filters results according to the context of the query.
• For example, if we search for assassination of Rafik
Hariri, then it omits results which have a time stamp
before that of the assassination date.
Cyc: Features
(cont.)
Qualitative Queries
Query: “Was Bill Clinton a good President of the
United States?”
• In cases like these, Cyc returns the results in a pros
and cons type and leave it to the user to make a
conclusion.
Queries With No Answer
Query: “At this instance of time, Is Alice inhaling or
Exhaling?”
• The Cyc system is intelligent enough to figure out
queries which can never be answered correctly.
Example taken from Google Techtalk by Doug Lenat, may 2006
The Dream
• The ultimate goal is to build enough common sense
into the Cyc system such that it can understand
Natural Language.
• Once it understands Natural Language, all the
system has to do is crawl through all the online
material and learn new common sense rules and
evolve.
• This two step process of building common sense
and using machine learning techniques to learn new
things will make the Cyc system an infinite source of
knowledge.
Drawbacks
• There is no single Ontology that works in all cases.
• Although Cyc is able to simulate common sense it
cannot distinguish between facts and fiction.
• In Natural Language Processing there is no way the
Cyc system can figure out if a particular word is used
in the normal sense or in the sarcastic sense.
• Adding knowledge is a very tedious process.
References
1. Marvin Minsky, Why People Think Computers Can’t, AI
Magazine, vol. 3 no. 4, Fall 1982.
2. Douglas B Lenat, Keynote address: computers vs common
sense, Proceedings of the 1991 ACM SIGMOD international
conference on Management of data, April 1991.
3. Douglas B Lenat, R V Guha, Karen Pittman, Dexter Pratt and
Mary Shepherd, Cyc: toward programs with common sense,
Communications of the ACM, 1990.
4. Douglas B Lenat, George Miller and Toshio Yokoi, CYC,
WordNet, and EDR: critiques and responses,
Communications of the ACM, 1995.
5. Talk by Douglas Lenat, Google techtalks, May 2006