Knowledge Acquisition by an Intelligent Acting Agent Michael Kandefer and Stuart Shapiro Department of Computer Science and Engineering, Center for Cognitive Science, and The National Center for Multisource Information Fusion Logical Formalizations of Commonsense Reasoning AAAI 2007 Spring Symposia March 28, Stanford University, California

Outline Problem Description and Requirements Basic algorithm Example Interactions with Agent Architecture for agent embodiment (GLAIR) Symbol anchoring in GLAIR Solution Details Conclusions

McCarthy’s Second Telephone Problem McCarthy (1979)* presents several problems that require commonsense reasoning. Second telephone problem: –A computer program that wants to telephone someone must reason about who knows the number. More generally, it must reason about what actions will obtain needed knowledge. Knowledge in books and computer files must be treated in a parallel way to knowledge held by persons. [Furthermore, a] program must often determine that it does not know something or that someone else doesn’t. * McCarthy, J First order theories of individual concepts and propositions. In Hayes, J.; Michie, D.; and Mikulich, L., eds., Machine Intelligence 9. Ellis Horwood Ltd

Problem Requirements 1.Determining the absence of the requisite knowledge for completing the task at hand. 2.Determining the external information sources that might contain the required information. 3.Knowing how to consult the source(s). 4.Retrieving and representing the needed information. 5.Completing the task with the acquired information.

External Sources Text files: –Telephone book: John 305 Pine Tree Lane –Campus telephone directory: Bill NSC User is used as an external source –User can type English statements asserting knowledge –User can enter a phone number when queried by the agent

Basic Calling Procedure 1.Believe none of the information sources have been searched 2.If the individual’s phone number is known, dial it. 3.Otherwise, search each information source that is believed to contain the phone number, dial it if found.

Example Interactions : What is Mike’s telephone number? Mike’s telephone number is : Call Mike. I am pressing 5. I am pressing 5. I am pressing 5. I am pressing 5. I am pressing 6. I am pressing 1. I am pressing 2.

Example Interactions : What is Stu’s telephone number? I don’t know. : Call Stu. Checking Campus Database for Stu’s telephone number. I am pressing 5. I am pressing 5. I am pressing 5. I am pressing 7. I am pressing 8. I am pressing 9. I am pressing 0. : What is Stu’s telephone number? Stu’s telephone number is

Example Interactions : What is Albert’s telephone number? I don’t know. : Call Albert. Checking Phonebook for Albert’s telephone number... I could not find Albert’s phone number in any external information source available to me. Do you know Albert’s number? Yes What is Albert’s number (e.g )? I am pressing 5. I am pressing 5. I am pressing 5. I am pressing 1. I am pressing 2. I am pressing 3. I am pressing 4. : What is Albert’s telephone number? Albert’s telephone number is

GLAIR Grounded Layered Architecture with Integrated Reasoning –Architecture for embodied agents. Knowledge layer (KL) –Conscious reasoning –Beliefs, Plans and Policies Perceptuo-motor layer (PML) –Primitive actions –Link to embodiment Sensory-actuator layer (SAL) –Sensors and actuators KL PML SAL

Implementing the KL SNePS-based agents have first-person belief base Structured as a set of propositions in a “language of thought” independent of the natural language used to express these beliefs SNePSLOG – higher-order logical language interface to SNePS

GLAIR and Symbol Anchoring KLPMLSAL SNePSLOG terms: Mikelex Lexicon: (“Mikelex“ ((ctgy. npr) (root. “Mike"))) English: “Mike” SNePSLOG expressions: Has(b5,compCat(lex(telephonelex), lex(numberlex)), b10). Name(b5, Mikelex). Value(b10,N(n5,N(n5,N(n5,N(n5, N(n6,N(n1,n2))))))). Grammar: GATN English: “Mike’s telephone number is ‘ ’.” SNePSLOG prim. act terms: act(lex(presslex), n5) Attach-primaction: (attach-primaction lex(presslex) press) (press “5”) Sensors and actuators “I am pressing 5.” Presses button ‘5’ on simulated phone

Domain of Discourse Entities –Propositions –Acts –Policies –Things

Individual Constants Denoting Things Mikelex - Lexeme “Mike” telephonelex - Lexeme “telephone” numberlex - Lexeme “number” studentlex - Lexeme “student” professorlex - Lexeme “professor” digitlex - Lexeme “digit” sequencelex - Lexeme “sequence” presslex – Lexeme “press” bDB - A campus telephone directory bPB - A telephone directory n0-n9 - Digits 0-9 b5 – denotes the individual named “Mike” b10 – denotes the value of Mike’s telephone number.

Thing-denoting Functions lex(x) – the thing that can be expressed by the lexeme denoted by x compCat(c1,c2) – the complex category which is a subcategory of the category denoted by c2 and is modified by c1 Ex. compCat(lex(telephonelex),lex(numberlex))

Telephone Number Values N(d,n) - the sequence [d,n] –N(n5,N(n5,N(n5,N(n1,N(n2,N(n3,n4))))) Digit sequence axiom: all(d)(Member(d, lex(digitlex)) => all(n)({Member(n,lex(digitlex)), Member(n,compCat(lex(digitlex),lex(sequencelex)))} v=> Member(N(d,n), compCat(lex(digitlex),lex(sequencelex))))).

Proposition-valued Functions Possession –Has(p,r,o) – “p’s r is o”. –Name(e,pn) – the proper-name of entity e is pn. –Value(e,v) - the value of entity e is v. Ex. Has(b5,compCat(lex(telephonelex),lex(numberlex)), b10). Name(b5, Mikelex). Value(b10, N(n5,N(n5,N(n5,N(n5,N(n6,N(n1,n2))))))). Act definition: –ActPlan(a1,a2): To perform act a1, perform act a2.

Act-denoting Functions (SNeRE) SNeRE acting system used for an online acting solution SNeRE primitive acts –snsequence(a1,a2) –snif({if(p1,a),...,if(pn,an)[,else(da)]}) –withsome(x, p(x), a(x), da) SNeRE Mental Acts: –disbelieve(p) –believe(p)

Primitive Act-denoting Functions Information retrieving primitives –search(p, s) –ask-user(p) Dialing Primitives –pickup() –putdown() –press(n)

Symbol Anchoring vs. Quotation Since: –propositions are first-class objects in the domain, –proposition-denoting functional terms are employed –alignment is used to connect lexeme-denoting terms with the actual lexemes Therefore, there is no need for a quotation or string operator.

Belief Base Introspection Agent requires the ability to introspect on its belief base and act on the results Lack of knowledge reasoning is captured by the else clause of withsome Ex. withsome({n,n1}, (Has(b5,compCat(lex(telephonelex), lex(numberlex)),n1) and Value(n1,n)), dial(b5,n), lookup(b5))

Reasoning About External Information Sources Determining the source: all(p)(Member(p, lex(personlex)) => ({Member(p,lex(professorlex)), Member(p,lex(studentlex))} v=> Contains(bDB,compCat(lex(telephonelex),lex(numberlex)),p))). all(p)(Member(p, lex(personlex)) => ({˜Member(p,lex(professorlex)), ˜Member(p,lex(studentlex))} &=> Contains(bPB,compCat(lex(telephonelex),lex(numberlex)),p))). Using the source: withsome(i, (Member(i,compCat(lex(informationlex),lex(sourcelex))) and Contains(i,compCat(lex(telephonelex),lex(numberlex)),p) and ˜Searched(i)), …)

Using the Information Regardless of how the information is acquired the dial defined act is performed dial is a recursive procedure that processes the digit sequence one digit at a time, performing press on each.

Dial Defined Act all(a,v,pn)({Member(a,lex(personlex)), Value(v,pn), Has(a,compCat(lex(telephonelex), lex(numberlex)), v)} &=> ActPlan(dial(a,pn), ordered-press(pn))). all(n1,n2)(Member(N(n1,n2), compCat(lex(digitlex),lex(sequencelex))) => ActPlan(ordered-press(N(n1,n2)), snif({if(Member(n2,lex(digitlex)), snsequence(act(lex(presslex),n1), act(lex(presslex),n2))), else(snsequence(act(lex(presslex),n1), ordered-press(n2)))}))).

Conclusions Demonstrated the necessary elements to an embodied solution to McCarthy’s Second Telephone number using SNePS 1.Determining missing information with withsome 2.Determining external sources to check using rules 3.Consulting those sources through the GLAIR architecture 4.Used GLAIR to retrieve and represent the needed information by anchoring the symbols of the knowledge base with the English language 5.Executed the task using the SNePS acting system with integrated reasoning