– Between humans: (e.g., Natural Language)
– Between machines: (e.g., Binary Code)
– Between human and machine (e.g., Programming
Language, GUI)
– etc.

– Facts (Potentially Infinite)
– No computation required
– Communication can be prohibitively inefficient
• E.g., Table of multiplication facts rather than a procedure
– Finite Facts + Computation Procedure
– Computation procedure is finite and explicit
– Communication can become very efficient
• Concise representations allows for communication
(computations as needed) potentially infinite knowledge

• Origins are Pre-Aristotle
• A language for the explicit communication of one’s knowledge between humans
– Explicit Facts + General purpose inference procedure
• Frege (1879) develops modern symbolic logic, the predicate calculus , complete with proof theory a formal language…for pure thought
• Exploited by mathematicians and philosophers demanding tools for rigorous expression and formal, unambiguous human communication
• Becomes the foundation for mathematical reasoning.
•
Principal developmental constraint: Human cognitive processes

• Store and & communicate knowledge between humans
• Use finitely expressed procedures to infer & communicate a potentially infinite amount of otherwise implicit knowledge
• Like a Book?
– Can afford MORE EFFICIENT COMMUNICATION
• Like a Human?:
– Can in many cases compute faster and more accurately.

• Step 1: Formulation - Human Focus
– Human communicates with Human
• Step 2 : Program - Machine Focus
– Human conforms and communicates with the computer
• Step 3: Application - Human Focus
– Computer conforms and communicates with Human

• Human prepares to communicate with machine by formulating knowledge.
• Typically communicates with other humans for accuracy and validation.
• Logic is a classic and fundamental language.
• Identify Goal
– body of knowledge and
– form of human-machine communication for step 3 (input/output)
• Identify base knowledge
– explicit facts required to derive body of knowledge
• Identify computation procedure
– Explicit procedure “algorithm” used to derive subset of knowledge representing output given input and base knowledge

• Step 2 : Program
– Human conforms and communicates with machine
– Human reformulates in a language the computer can process
– Programming language becomes means of expression for human to communicate knowledge and computation procedure to machine.
• Step 3: Application
– Machine comforts to communicate with Human
– Human user “queries”/”dialogs” with computer providing input
– Computer executes program, derives, forms and communicates response to human

• Origins -- 1940’s
• Developed for Two Purposes:
– For Controlling the operations of a computer AND
– For humans to express knowledge
• Conflicting
Developmental Constraints:
– The con Neumann architecture
– Human cognitive processes
• Knowledge formulation in logic is not sufficient
• Must program the machine
– The “program” becomes the persistent artifact of communication not the human formulation

Objects and Attributes
Automatic Inference (Inheritance)
Automatic Memory Management
C
Functional Family
ALGOL Family
Logic Programming
Assembler
Programming Languages
Bridging the Gap
Logic
01011
01010
Designed for Formal Expression and Communication of Human Knowledge
Designed to operate a machine designed to add binary numbers

•
Program
– set of axioms
– declarative logic statement of knowledge
• Computation
– Constructive proof of goal statement from a program
– Not part of program -- an abstract machine -- like the accumulator in a VN
• Answer: “natural” logic consequent of the program
•
Original Motivation
– Desire to automate the proof procedure of predicate calculus so that theorems could be automatically generated by the computer
– In general, however, this is not possible (proof by Godel)
•
Positive Impact
– For a class of interesting problems a limited proof procedure can be useful
– A declarative logic-based language can afford precise logical syntax and semantics based on general-purpose and “natural” computation procedure
– A classic logic-based expression is transformed into effective computation

Historical Highlights
• 1965: Robinson’s Unification Algorithm and Resolution Principle mechanizes proof procedure
• 1970’s:
– Kowaski formulates a procedural interpretation of Horn Clause Logic. Shows that an axiom of the form:
• A if B1 and B2 and …BN
• Can be interpreted and executed as a procedure of a recursive program based on resolution proof procedure with unification algorithm.
– Colmerauer develops Prolog based on Kowlaski’s procedural interpretation
– vanEmden and Kowalski develop formal semantics for Logic Programming
– Warren dispels myths of impracticability with an efficient implementation based on easily programmed abstract machine (the WAM)
• 1980’s -
– Advanced techniques execute more “logical specs” as efficient programs
– Constraint Logic Programming as a generalization of LP allows for a framework that maintains declarative logic syntax and semantics while solving classes of CSPs as efficiently as theoretically possible.

•
Ideal
– A problem decoratively stated in symbolic logic executes on a vonNeuman machine as efficient program
• Benefits
– Programming becomes classic logical specification and inherits the known benefits of formal logic as language (precise semantics and natural inference mechanism)
– Intent and correctness of programs are more easily accessible, understood and validated by humans (and by machines)
– Expression/Communication of domain/problem knowledge is not cluttered with knowledge about the machine or about the idiosyncrasies of managing machine resources.
• Reality
– Currently not ALL clear, elegant logic specifications in LP execute as efficient logic programs
• None the less LP allows for specialized algorithms to be expressed as declarative logic programs.
– Research in LP is focused on broaden the class of specs that do execute as efficient logic programs

• Course focus
– AI and Knowledge Representation and Automated Reasoning
– How computers can help HUMANS capture, compute and communicate knowledge.
• Logic Programming
– H as close ties to formal logic - the tried and true foundation for the formal representation and communication of human knowledge
– Provides a programming foundation for formally representing knowledge and inference
– A llows us , as software engineers, to write programs ignoring machine issues (e.g., memory struc ture and management) and to concentrate on knowledge representation, human reasoning and formal human communication.

• Encompass a broad range of AI topics and techniques
•
Documents are themselves human communication artifacts
– They are produced by some process
– The represent and communicate knowledge through language
•
The act of authoring documents can range
– From the very creative (fictional literature)
– To the very unimaginative and simplistic (filling in a form)
– And include things in between like finding, weaving together and refining previous written text to form a new document
• Exploring how we might model the human processes involved in document authoring requires ideas about how we might represent and reason about knowledge, language and the human creative processes.
• Great foundation for exploring the potential and the limits of the role of computers in human creativity and communication

• Logical Foundations
– Properties and Mechanics of Classic Symbolic Logic
• Logic Programming and PROLOG
– Practical tool kit for implementing knowledge-based systems
• Knowledge Representation
– Basic techniques for modeling common-sense domains
• Three Applications
– Brutus -- Creative Writing
– DocCon -- Business document authoring, knowledge management and reuse
– Test Generation

• Background
– Academic research in automatic story generation
– Designed to explore computational models of human creative authorship
– Developed in Prolog (FLEX)
• Focus
– What kinds of knowledge is represented
– What kinds of processes are modeled
– What kinds of automatic reasoning techniques are used
– How is natural language used
• Students will have an opportunity to explore and extend the code

• Background
– Commercial System developed at an RPI incubator company,
Document
Development Corporation , in collaboration with IBM Research
– Designed to assist in managing and reusing document knowledge to assist in authoring business documents
– Carefully engineered and developed a variety of languages (Java, C,
Delphi)
• Focus
– Business Problem
– System architecture
– Document Knowledge Representation Scheme
– Interactive Document Configuration
• Students will have an opportunity to develop authoring solutions for a class of documents using DocCon

%--------------------------------------------------------------
%Version 0
% - Direct Rep of logic
% - Does not terminate (Try :- closure(1,3) for test0)
% - Inefficient
%-------------------------------------------------------------closure0(X,Y) :- base(X,Y).
closure0(X,Z) :- closure0(X,Y), closure0(Y,Z).
%arcs in graph
%transitive step

%--------------------------------------------------------------
%Version 1
% - A little less Direct Rep of logic
% - Terminates for acyclic graphs
% - works on test0 but not on test1.
% - Still Inefficient
%-------------------------------------------------------------closure1(X,Y) :- base(X,Y).
closure1(X,Z) :- base(X,Y), closure1(Y,Z).

%--------------------------------------------------------------
%Version 2
% - A little less direct rep of logic
% - Terminates for cyclic and acyclic graphs
% - skips visited nodes
% - works on test0 and test1
% - Inefficient - Traverses all paths when only need one.
%-------------------------------------------------------------closure2(X,Y) :closure2a(X,Y,[X]).
closure2a(X,Y,_) :- base(X,Y).
closure2a(X,Z,Avoiding) :base(X,Y), not(on(Y,Avoiding)), closure2a(Y,Z,[Y | Avoiding]).

%--------------------------------------------------------------
%Version 3
% - Warshalls algorithm for transitive closure in Prolog
% - Indirect Rep of logic
% - Terminates for cyclic and acyclic graphs
% - Efficient (Does not visit unnecessary paths)
%--------------------------------------------------------------
% "Very often you will be tempted to think that it is time
% to abandon Prolog for C. Often you will do better to replace
% a specification (in Prolog) by a program (in Prolog)."
%
% Good algorithms are important no matter what programming Language.