Knowledge Representation and Reasoning

1
Knowledge Representation and Reasoning
Master of Science in Artificial Intelligence,
2009-2011

• University "Politehnica" of Bucharest
• Department of Computer Science
• Fall 2009
• Adina Magda Florea
• http//turing.cs.pub.ro/krr_09
• curs.cs.pub.ro

2
Lecture 1

• Lecture outline
• Course goals
• Grading
• Textbooks and readings
• AI well known companies
• Syllabus
• Why KR?
• KRR Challenges
• What is KRR?
• Formal logic why and how
• Links for the young researcher

3
Course goals

• Provide an overview of existing representational
  frameworks developed within AI, their key
  concepts and inference methods.
• Acquiring skills in representing knowledge
• Understanding the principles behind diferent
  knowledge representation techniques
• Being able to read and understand research
  literature in the area of KRR
• Being able to complete a project in this research
  area

4
Grading

• Course grades Mid-term exam               20
  Final exam                     30 Projects 
  30Laboratory                   
  20
• Requirements min 7 lab attendances, min 50 of
  term activity (mid-term ex, projects, lab)
• Academic Honesty Policy It will be considered an
  honor code violation to give or use someone
  else's code or written answers, either for the
  assignments or exam tests. If such a case occurs,
  we will take action accordingly.

5
Textbooks and Readings

• Textbooks
• Artificial Intelligence A Modern Approach (2003,
  2009) by Stuart Russell and Peter Norvig
• Computational Intelligence a Logical Approach by
  David Poole, Alain Mackworth, and Randy Goebel,
  Oxford University Press, 1998
• Readings
• Reading materials will be assigned to you.
• You are expected to do the readings before the
  class

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AI well known companies

• Cycorp, Inc. http//www.cyc.com/
• Cycorp was founded in 1994 to research, develop,
  and commercialize Artificial Intelligence.
  Cycorp's vision is to create the world's first
  true artificial intelligence, having both common
  sense and the ability to reason with it.
• Soar Technology, Inc. http//www.soartech.com/
• Design of "highly human" intelligent agents
• Autonomous Decision Making Software
  http//www.agent-software.com/
• Franz Inc. http//www.franz.com/
• Enterprise Development Tools (Allegro CL)
• http//www.franz.com/enterprise_development_tools.
  lhtml
• Semantic Web Technologies (AllegroGraph,
  RacerPro)
• http//www.franz.com/agraph/

Drive Syllabus
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Syllabus

• 1. General knowledge representation issues
• Readings
• http//plato.stanford.edu/entries/logic-ai/
• 2. Logical agents Logical knowledge
  representation and reasoning
• First order predicate logic revisited, ATP
  Lect. 2
• Readings
• AIMA Chapter 7 http//aima.cs.berkeley.edu/newch
  ap07.pdf
• Nonmonotonic logics and reasoning Lect. 4
• Readings
• Non-monotonic Logic, Stanford Encyclopedia of
  Philosophy http//plato.stanford.edu/entries/logi
  c-nonmonotonic/
• Nonmonotonic Reasoning, G. Brewka, I. Niemela,
  M. Truszczynski
• http//www.informatik.uni-leipzig.de/brewka/pap
  ers/NMchapter.pdf
• Nonmonotonic Reasoning With WebBased Social
  Networks
• http//www.mindswap.org/katz/papers/socialnet-d
  efaults.pdf

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Syllabus

• Modal logic, logics of knowledge and beliefs
  Lect 5
• Readings Modal logic on Wikipedia
• http//en.wikipedia.org/wiki/Modal_logic
• to be announced
• Semantic networks and description logics,
  reasoning services Lect 6
• Readings to be announced
• Knowledge representation for the Semantic Web
  Lect. 7
• Readings
• Ontology knowledge representation - from
  description logic to OWL Description Logics as
  Ontology Languages for the Semantic Web
• http//lat.inf.tu-dresden.de/research/papers/2005/
  BaSaJS60.pdf

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Syllabus

• Midterm exam (written examination) 1h
• 3. Rule based agents
• Rete Efficient unification Lect. 8
• Readings
• The RETE algorithm
• http//www.cis.temple.edu/ingargio/cis587/readin
  gs/rete.html
• The Soar model, universal subgoaling and chunking
  Lect. 9, 10
• Readings
• A gentle introduction to Soar, an architecture
  for human cognition
• http//ai.eecs.umich.edu/soar/sitemaker/docs/misc/
  GentleIntroduction-2006.pdf
• Modern rule based systems Lect. 11

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Syllabus

• 4. Probabilistic agents
• Probabilistic knowledge representation and
  reasoning Lect. 12
• Readings
• to be announced
• Rule based methods for uncertain reasoning
  Lect. 13
• Readings
• to be announced
• 5. Intelligence without representation and
  reasoning vs. Strong AI Lect. 14
• Final exam (oral examination)

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Why KR?

• We understand by "knowledge" all kinds of facts
  about the world.
• Knowledge is necessary for intelligent behavior
  (human beings, robots).
• What is knowledge? We shall not try to answer
  this question!
• Instead, in this course we consider
  representations of knowledge and how we can use
  it in making intelligent artifacts.

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KRR Challenges

• Challenges of KRR
• representation of commonsense knowledge
• the ability of a knowledge-based system to
  tradeoff computational efficiency for accuracy of
  inferences
• its ability to represent and manipulate uncertain
  knowledge and information.

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What is KR?

• Randall Davis, Howard Shrobe, Peter Szolovits,
  MIT
• A knowledge representation is most fundamentally
  a surrogate, a substitute for the thing itself,
  used to enable an entity to determine
  consequences by reasoning about the world.
• It is a set of ontological commitments, i.e., an
  answer to the question In what terms should I
  think about the world?

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What is KR?

• It is a fragmentary theory of intelligent
  reasoning, expressed in terms of three
  components
• the representation's fundamental conception of
  intelligent reasoning
• the set of inferences the representation
  sanctions
• the set of inferences it recommends.

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What is KR?

• It is a medium for pragmatically efficient
  computation, i.e., the computational environment
  in which reasoning is accomplished.
• One contribution to this pragmatic efficiency is
  supplied by the guidance a representation
  provides for organizing information so as to
  facilitate making the recommended inferences.
• It is a medium of human expression, i.e., a
  language in which we say things about the world.

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What is KR?

• If A represents B, then A stands for B and is
  usually more easily accessible than B.
• We are interested in symbolic representations
• Symbolic representations of propositions or
  statements that are believed by some agent.

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What is Reasoning?

• Not interested (in the course) in the
  philosophical dimension
• Reasoning is the use of symbolic representations
  of some statements in order to derive new ones.
• While statements are abstract objects, their
  representations are concrete objects and can be
  easily manipulated.

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What is Reasoning?

• Reasoning can be as easy as mechanical symbol
  manipulation.
• or as http//plato.stanford.edu/entries/logical-co
  nsequence/
• Reasoning should scale well we need efficient
  reasoning algorithms.

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Formal logic

• Formal logic is the field of study of entailment
  relations, formal languages, truth conditions,
  semantics, and inference.
• All propositions/statements are represented as
  formulae which have a semantics according to the
  logic in question.
• Logical system Formal language semantics
• Formal logics gives us a framework to discuss
  different kinds of reasoning.

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Logical consequence (entailment)

• Proof centered approach to logical consequence
  the validity of a reasoning process (argument)
  amounts to there being a proof of the conclusions
  from the premises.

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Logical consequence (entailment)

• Model centered approach to logical consequence
• Models are abstract mathematical structures that
  provide possible interpretations for each of the
  non-logical primitives in a formal language.
• Given a model for a language - define what it is
  for a sentence in that language to be true
  (according to that model) or not.
• In any model in which the premises are true the
  conclusion is true too. (Tarski's definition of
  logical consequence from 1936.)

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Properties of logical systems

• Important properties of logical systems
• Consistency - no theorem of the system
  contradicts another.
• Soundness - the system's rules of proof will
  never allow a false inference from a true
  premise. If a system is sound and its axioms are
  true then its theorems are also guaranteed to be
  true.
• Completeness - there are no true sentences in the
  system that cannot, at least in principle, be
  proved in the system.
• Some logical systems do not have all three
  properties. Kurt Godel's incompleteness theorems
  show that no standard formal system of arithmetic
  can be consistent and complete.

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Important notions in logical systems

• Model of a formula
• Entailment or logical implication
• Theorem, deduction
• Th(?) set of provable theorems in ?
• Monotonicity
• Idempotence - multiple applications of the
  operation do not change the result
• Th(?) a fixed point operator which computes the
  closure of a set of formulas ? according to the
  rules of inference
• Th(?) the least fixed point of this closure
  process
• Important theorems for entailment

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Important notions in logical systems

• A logical system L is complete iff
• ? ? L ? implies ? ??
• (i.e., all valid formulas are provable)
• A logical system L is sound iff
• ? ?? implies ? ? L ?
• (i.e., no invalid formula is provable)
• FOPL
• Second order logics

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Links for the young researcher

• AI-MAS Links of interest
• Academic publishing
• http//en.wikipedia.org/wiki/Academic_publishing
• Writing a Scientific Paper
• http//www.oup.com/us/samplechapters/0841234620/?v
  iewusa
• ISI Web of Knowledge
• http//isiwebofknowledge.com/
• Master Journal List
• http//science.thomsonreuters.com/mjl/
• Conference Proceedings Citation Index
• http//wokinfo.com/products_tools/multidisciplinar
  y/webofscience/cpci/
• TED Ideas worth spreading
• http//www.ted.com/