Structuring Knowledge & Data in AI Programs
AITopics > Representation
Computers, unfortunately, are not as adept at forming internal representations of the world. ... Instead of gathering knowledge for themselves, computers must rely on human beings to place knowledge directly into their memories.
This suggests programming, but even before programming begins, we must decide on ways to represent information, knowledge, and inference techniques inside a computer."
- Arnold, William R. and John S. Bowie. 1985. Artificial Intelligence: A Personal Commonsense Journey. Englewood Cliffs, NJ: Prentice Hall. Excerpt taken from the Introduction to Chapter 3 at page 46.
Definition of the Area
"What is a knowledge representation? We argue that the notion can best be understood in terms of five distinct roles that it plays, each crucial to the task at hand:
From What Is a Knowledge Representation? by Randall Davis, Howard Shrobe, and Peter Szolovits. Downloadable PDF file from AI Magazine (Spring, 1993).
What is A Knowledge Representation? Randall Davis, Howard Shrobe, and Peter Szolovits. AI Magazine 14(1): Spring 1993, 17-33. "What is a knowledge representation? We argue that the notion can best be understood in terms of five distinct roles it plays, each crucial to the task at hand:
Knowledge Representation: Logical, Philosophical, and Computational Foundations. By John Sowa. 2000. Pacific Grove: Brooks/Cole. "Knowledge representation is a multidisciplinary subject that applies theories and techniques from three other fields: 1. Logic provides the formal structure and rules of inference. 2. Ontology defines the kinds of things that exist in the application domain. 3. Computation supports the applications that distinguish knowledge representation from pure philosophy." - from the Preface.
Computational Intelligence - A Logical Approach. By David Poole, Alan Mackworth and Randy Goebel. 1998. Oxford University Press, New York. "In order to use knowledge and reason with it, you need what we call a representation and reasoning system (RRS). A representation and reasoning system is composed of a language to communicate with a computer, a way to assign meaning to the language, and procedures to compute answers given input in the language. Intuitively, an RRS lets you tell the computer something in a language where you have some meaning associated with the sentences in the language, you can ask the computer questions, and the computer will produce answers that you can interpret according to the meaning associated with the language. ... One simple example of a representation and reasoning system ... is a database system. In a database system, you can tell the computer facts about a domain and then ask queries to retrieve these facts. What makes a database system into a representation and reasoning system is the notion of semantics. Semantics allows us to debate the truth of information in a knowledge base and makes such information knowledge rather than just data." - excerpt from Chapter 1 (pages 9 - 10).
Computers versus Common Sense. Video (May 30, 2006; 1 hour, 15 minutes) from Google TechTalks. Dr. Douglas Lenat, President and CEO of Cycorp, talks about common sense: "It's way past 2001 now, where the heck is HAL? ... What's been holding AI up? The short answer is that while computers make fine idiot savants, they lack common sense: the millions of pieces of general knowledge we all share, and fall back on as needed, to cope with the rough edges of the real world. I will talk about how that situation is changing, finally, and what the timetable -- and the path -- realistically are on achieving Artificial Intelligence."
Turing’s Dream and the Knowledge Challenge. Video (November 10, 2005; 58 minutes) from the 2006 University of Washington Computer Science & Engineering Colloquium Series, available from ReseachChannel. "In this Turing Center distinguished lecture, Lenhart Schubert [University of Rochester] explains that there is a set of clear-cut challenges for artificial intelligence, all centering around knowledge. The solution to those challenges could realize Alan M. Turing's dream - the dream of a machine capable of intelligent human-like response and interaction. Schubert presents preliminary results of recent efforts to extract 'shallow' general knowledge about the world from large text corpora."
Lesson: Object-Oriented Programming Concepts. Part of The Java Tutorial available from Sun Microsystems. "If you've never used an object-oriented language before, you need to understand the underlying concepts before you begin writing code. You need to understand what an object is, what a class is, how objects and classes are related, and how objects communicate by using messages. The first few sections of this trail describe the concepts behind object-oriented programming. The last section shows how these concepts translate into code."
Knowledge Representation research at the Computational Intelligence Research Laboratory (CIRL) at the University of Oregon. "Knowledge representation (KR) is the study of how knowledge about the world can be represented and what kinds of reasoning can be done with that knowledge. Important questions include the tradeoffs between representational adequacy, fidelity, and computational cost, how to make plans and construct explanations in dynamic environments, and how best to represent default and probabilistic information." In addition to the helpful Pointers, be sure to follow the links to Subareas at the bottom of their pages for additional information.
The Semantic Web. By Tim Berners-Lee, James Hendler, and Ora Lassila. Scientific American (May 2001). "Traditional knowledge-representation systems typically have been centralized, requiring everyone to share exactly the same definition of common concepts such as 'parent' or 'vehicle.' But central control is stifling, and increasing the size and scope of such a system rapidly becomes unmanageable."
Diagrammatic Reasoning: Cognitive and Computational Perspectives. Edited by Janice Glasgow, N. Hari Narayanan, and B. Chandrasekaran. AAAI Press. The following excerpt is from the Foreword by Herbert Simon: "That reasoning using language and using diagrams were different, at least in important respects, was brought home by the Pythagorean discovery of irrational numbers. ... Words, equations, and diagrams are not just a machinery to guarantee that our conclusions follow from their premises. In their everyday use, their real importance lies in the aid they give us in reaching the conclusions in the first place."
Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language. Edited by Lucja M. Iwanska and Stuart C. Shapiro. AAAI Press. The following excerpt is from the Preface: "The research direction of natural language-based knowledge representation and reasoning systems constitutes a tremendous change in how we view the role of natural language in an intelligent computer system. The traditional view, widely held within the artificial intelligence and computational linguistics communities, considers natural language as an interface or front end to a system such as an expert system or knowledge base. In this view, inferencing and other interesting information and knowledge processing tasks are not part of natural language processing. By contrast, the computational models of natural language presented in this book view natural language as a knowledge representation and reasoning system with its own unique, computationally attractive representational and inferential machinery. This new perspective sheds some light on the actual, still largely unknown, relationship between natural language and the human mind. Taken to an extreme, such approaches speculate that the structure of the human mind is close to natural language. In other words, natural language is essentially the language of human thought."
Alternative Representations: Neural Nets and Genetic Algorithms. Section 1.2.9 of Chapter One (available online) of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005). "Most of the techniques presented in this AI book use explicitly represented knowledge and carefully designed search algorithms to implement intelligence. A very different approach seeks to build intelligent programs using models that parallel the structure of neurons in the human brain or the evolving patterns found in genetic algorithms and artificial life."
Programs with Common Sense. A classic paper by John McCarthy (1959). "This paper will discuss programs to manipulate in a suitable formal language (most likely a part of the predicate calculus) common instrumental statements. The basic program will draw immediate conclusions from a list of premises. These conclusions will be either declarative or imperative sentences. When an imperative sentence is deduced the program takes a corresponding action. These actions may include printing sentences, moving sentences on lists, and reinitiating the basic deduction process on these lists."
Claude E. Shannon: Founder of Information Theory. By Graham P. Collins. Scientific American Explore (October 14, 2002). "Shannon's M.I.T. master's thesis in electrical engineering has been called the most important of the 20th century: in it the 22-year-old Shannon showed how the logical algebra of 19th-century mathematician George Boole could be implemented using electronic circuits of relays and switches. This most fundamental feature of digital computers' design -- the representation of 'true' and 'false' and '0' and '1' as open or closed switches, and the use of electronic logic gates to make decisions and to carry out arithmetic -- can be traced back to the insights in Shannon's thesis."
The St. Thomas Common Sense Symposium: Designing Architectures for Human-Level Intelligence. By Marvin Minsky, Push Singh, and Aaron Sloman. AI Magazine 25(2): Summer 2004, 113-124. Abstract: "To build a machine that has "'common sense' was once a principal goal in the field of artificial intelligence. But most researchers in recent years have retreated from that ambitious aim. Instead, each developed some special technique that could deal with some class of problem well, but does poorly at almost everything else. We are convinced, however, that no one such method will ever turn out to be 'best,' and that instead, the powerful AI systems of the future will use a diverse array of resources that, together, will deal with a great range of problems. To build a machine that's resourceful enough to have humanlike common sense, we must develop ways to combine the advantages of multiple methods to represent knowledge, multiple ways to make inferences, and multiple ways to learn. We held a two-day symposium in St. Thomas, U.S. Virgin Islands, to discuss such a project --- to develop new architectural schemes that can bridge between different strategies and representations. This article reports on the events and ideas developed at this meeting and subsequent thoughts by the authors on how to make progress."
Logical Versus Analogical or Symbolic Versus Connectionist or Neat Versus Scruffy. By Marvin Minsky. AI Magazine AI Magazine 12(2): Summer 1991, 34-51. "Engineering and scientific education condition us to expect everything, including intelligence, to have a simple, compact explanation. Accordingly, when people new to AI ask 'What’s AI all about,' they seem to expect an answer that defines AI in terms of a few basic mathematical laws. Today, some researchers who seek a simple, compact explanation hope that systems modeled on neural nets or some other connectionist idea will quickly overtake more traditional systems based on symbol manipulation. Others believe that symbol manipulation, with a history that goes back millennia, remains the only viable approach. Marvin Minsky subscribes to neither of these extremist views. Instead, he argues that AI must use many approaches. AI is not like circuit theory and electromagnetism. There is nothing wonderfully unifying like Kirchhoff’s laws are to circuit theory or Maxwell’s equations are to electromagnetism. Instead of looking for a 'right way,' the time has come to build systems out of diverse components, some connectionist and some symbolic, each with its own diverse justification."
A Framework for Representing Knowledge. By Marvin Minsky. MIT- AI Laboratory Memo 306, June, 1974. Reprinted in The Psychology of Computer Vision, P. Winston (Ed.), McGraw-Hill, 1975. Shorter versions in J. Haugeland, Ed., Mind Design, MIT Press, 1981, and in Cognitive Science, Collins, Allan and Edward E. Smith (eds.) Morgan-Kaufmann, 1992. "It seems to me that the ingredients of most theories both in Artificial Intelligence and in Psychology have been on the whole too minute, local, and unstructured to account -- either practically or phenomenologically -- for the effectiveness of common-sense thought. The 'chunks' of reasoning, language, memory, and 'perception' ought to be larger and more structured; their factual and procedural contents must be more intimately connected in order to explain the apparent power and speed of mental activities. ... I try here to bring together several of these issues by pretending to have a unified, coherent theory. The paper raises more questions than it answers, and I have tried to note the theory's deficiencies.
Here is the essence of the theory: When one encounters a new situation (or makes a substantial change in one's view of the present problem) one selects from memory a structure called a Frame. This is a remembered framework to be adapted to fit reality by changing details as necessary.
A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party."
Enabling Technology For Knowledge Sharing. Robert Neches, Richard Fikes, Tim Finin, Thomas Gruber, Ramesh Patil, Ted Senator, and William R. Swartout. AI Magazine 12(3): Fall 1991, 36-56.
The Knowledge Level. By Allen Newell. AAAI Presidential Address, 19 August 1980. AI Magazine 2(2): Summer 1981, 1-20, 33. A classic article describing the differences in viewing computer programs at the symbol level or the knowledge level.
Stewart, Doug. Interview with Herbert Simon, (June 1994). Omni Magazine archives.
Logical Agents. Chapter 7 of the textbook, Artificial Intelligence: A Modern Approach (Second Edition), by Stuart Russell and Peter Norvig. "This chapter introduces knowledge-based agents. The concepts that we discuss -- the representation of knowledge and the reasoning processes that bring knowledge to life -- are central to the entire field of artificial intelligence. ... We begin in Section 7.1 with the overall agent design. Section 7.2 introduces a simple new environment, the wumpus world, and illustrates the operation of a knowledge-based agent without going into any technical detail. Then, in Section 7.3, we explain the general principles of logic. Logic will be the primary vehicle for representing knowledge throughout Part III of the book."
Logic and Artificial Intelligence. Entry by Richmond Thomason. The Stanford Encyclopedia of Philosophy (Fall 2003 Edition); Edward N. Zalta, editor. "1.2 - Knowledge Representation In response to the need to design this declarative component, a subfield of AI known as knowledge representation emerged during the 1980s."
AITopics pages on representing the meaning of text in markup languages for the web.
AI on the Web: Logic and Knowledge Representation. A resource companion to Stuart Russell and Peter Norvig's "Artificial Intelligence: A Modern Approach" with links to reference material, people, research groups, books, companies and much more.
Cognitive Systems for Cognitive Assistants (CoSY), an EU FP6 IST Cognitive Systems Integrated project. "The main goal of the project is to advance the science of cognitive systems through a multi-disciplinary investigation of requirements, design options and trade-offs for human-like, autonomous, integrated, physical (eg., robot) systems, including requirements for architectures, for forms of representation, for perceptual mechanisms, for learning, planning, reasoning and motivation, for action and communication."
Knowledge Systems Research at AIAI, the Artificial Intelligence Applications Institute at the University of Edinburgh's School of Informatics. "AIAI's Knowledge Systems Research concentrates on those areas of Artificial Intelligence that are concerned with explicit representations of knowledge. These are Knowledge Representation, including Ontologies, Enterprise Modelling, and Knowledge Management; Knowledge Engineering, including tools for acquiring formal models and checking their structure; and, more recently, services and brokering on the Semantic Web."
Other References Offline
Allen, J. F. 1991. Time and Time Again: The Many Ways to Represent Time. International Journal of Intelligent Systems 6: 341-355.
Barr, Avron, and Edward A. Feigenbaum, editors. 1981. The Handbook of Artificial Intelligence, Volume 1: 143. (Reading, MA: Addison-Wesley, 1989)
Brachman, Ronald, and Hector Levesque. 2004. Knowledge Representation and Reasoning. Morgan Kaufmann (part of Elsevier’s Science and Technology Division). Excerpt from the publisher's description: "Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail."
Brachman, R. J., and H. J. Levesque, editors. 1985. Readings in Knowledge Representation. San Mateo, CA: Morgan Kaufmann.
Davis, E. 1990. Representations of Commonsense Knowledge. San Mateo, CA: Morgan Kaufmann.
Hayes, Patrick J. 1995. In Defense of Logic. In Computation and Intelligence: Collected Readings, ed. Luger, George F., 261-273. Menlo Park/Cambridge, MA/London: AAAI Press/The MIT Press.
Hendrix, G. 1979. Encoding Knowledge in Partitioned Networks. In Associative Networks, ed. Findler, N., 51-92. New York: Academic Press.
Holmes, Bob. 1999. Beyond Words. New Scientist Magazine (7/10/99). "[V]isual language works better for some kinds of information than for others. 'It's best at being able to grasp things in context and see how they're related,' says Terry Winograd, a computer scientist who directs the Program on People, Computers, and Design at Stanford University. 'It's correspondingly less good at precision and detail."
The Charlie Rose Show (December 21, 2004): A panel discussion about Artificial Intelligence, with Rodney Brooks (Director, MIT Artificial Intelligence Laboratory & Fujitsu Professor of Computer Science & Engineering, MIT), Eric Horvitz (Senior Researcher and Group Manager, Adaptive Systems & Interaction Group, Microsoft Research), and Ron Brachman (Director, Information Processing Technology Office, Defense Advanced Research Project Agency, and President, American Association for Artificial Intelligence). "Rose: What do you think has been the most important advance so far? Brachman: A lot of people will vary on that and I'm sure we all have different opinions. In some respects one of the - - - I think the elemental insights that was had at the very beginning of the field still holds up very strongly which is that you can take a computing machine that normally, you know, back in the old days we think of as crunching numbers, and put inside it a set of symbols that stand in representation for things out in the world, as if we were doing sort of mental images in our own heads, and actually with computation, starting with something that's very much like formal logic, you know, if-then-else kinds of things, but ultimately getting to be softer and fuzzier kinds of rules, and actually do computation inside, if you will, the mind of the machine, that begins to allow intelligent behavior. I think that crucial insight, which is pretty old in the field, is really in some respects one of the lynch pins to where we've gotten. ... Horvitz: I think many passionate researchers in artificial intelligence are fundamentally interested in the question of Who am I? Who are people? What are we? There's a sense of almost astonishment at the prospect that information processing or computation, if you take that perspective, could lead to this. Coupled with that is the possibility of the prospect of creating consciousnesses with computer programs, computing systems some day. It's not talked about very much at formal AI conferences, but it's something that drives some of us in terms of our curiosity and intrigue. I know personally speaking, this has been a core question in the back of my mind, if not the foreground, not on my lips typically, since I've been very young. This is this question about who am I. Rose: ... can we create it? Horvitz: Is it possible - - - is it possible that parts turning upon parts could generate this?"
Schank, Roger C. 1995. The Structure of Episodes in Memory. In Computation and Intelligence: Collected Readings, ed. Luger, George F., 236-259. Menlo Park/Cambridge, MA/London: AAAI Press/The MIT Press.
Stefik, Mark. 1995. Introduction to Knowledge Systems. San Francisco: Morgan Kaufmann.