Knowledge and Automatic Reasoning

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
Mail
La asignatura es una introducción a los conceptos básicos del razonamiento y su automatización. Comienza con una introducción a la lógica proposicional y de primer orden y la programación lógica. A partir de la caracterización lógica de las diversas formas de inferencia se introducen las variantes que permiten enfrentar el razonamiento aproximado bajo incertidumbre y ambigüedad. A partir de estos conocimientos se presentan y comparan las representaciones de conocimiento relacionales y las arquitecturas clásicas de los sistemas basados en conocimiento.

Teachers

Person in charge

  • Ramon Sangüesa Sole ( )

Weekly hours

Theory
2
Problems
1
Laboratory
1
Guided learning
0.4
Autonomous learning
5.6

Competences

Transversal Competences

Transversals

  • CT4 [Avaluable] - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
  • CT5 [Avaluable] - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.

Technical Competences

Especifics

  • CE02 - To master the basic concepts of discrete mathematics, logic, algorithmic and computational complexity, and its application to the automatic processing of information through computer systems . To be able to apply all these for solving problems.
  • CE15 - To acquire, formalize and represent human knowledge in a computable form for solving problems through a computer system in any field of application, particularly those related to aspects of computing, perception and performance in intelligent environments or environments.
  • CE18 - To acquire and develop computational learning techniques and to design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.

Generic Technical Competences

Generic

  • CG2 - To use the fundamental knowledge and solid work methodologies acquired during the studies to adapt to the new technological scenarios of the future.
  • CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
  • CG5 - Work in multidisciplinary teams and projects related to artificial intelligence and robotics, interacting fluently with engineers and professionals from other disciplines.

Objectives

  1. To know and understand the concept of logic
    Related competences: CE02, CE15,
  2. To understand, write and manipulate proficiently formulase in various logics (propositional logic, first order logic, description logics, fuzzy logics), with special emphasis on application
    Related competences: CG4, CE02, CE15, CE18,
  3. To know now how to apply logical foundations to the increasing number of applications of reasoning methods in computing.
    Related competences: CG2, CG4, CE02, CE15,
  4. To be able to analyze the knowledge that is necessary to solve a problem.
    Related competences: CG2, CG4, CG5, CT5, CE15, CE18,
  5. To be able to analyze a problem an decide which representation and reasoning techniques are the mos suitable to solve it
    Related competences: CG2, CG4, CG5, CT5, CE15,
  6. To be able to elicit and represent the necessary knowledge to build an application in the field of knowledge-based systems.
    Related competences: CG2, CG4, CG5, CT4, CT5, CE15, CE18,

Contents

  1. Introduction: Intelligence, Knowledge, Reason, Reasoning and Computing.
    Presentation of the role of reasoning in intelligence. Knowledge and its representation in relation to reasoning. The various types of knowledge: declarative (relational, heritable, inferable), procedural, implicit, a priori and actionable.
  2. Reasoning and logic
    Logic as a representation of knowledge. Logic as a reasoning mechanism. Logical closure.
  3. Propositional Logics.
    Introduction to logics and the basic concepts needed to characterize and use it: satisfaction, tautology, consequence and equivalence. Expressive power vs. computational cost. Deduction in Propositional Logic.
  4. First-order logic
    First-order logics: normal forms, literal forms and clauses. Expressive power and decidability. Properties of computational logic systems. Deduction in First Order Logic.
  5. Logic Programming.
    Introduction to logical programming answer calculation, resolution strategies, backtracking management.
  6. Knowledge-Based Systems: Basic Architectures
    Knowledge-based systems: similarities and differences with logical systems. Components of a knowledge-based system. Reasoning strategies.
  7. Approximate Reasoning
    Characterization of the approximate reasoning: Uncertain, imprecise, ambiguous. The conditioning factors of the approximate reason. Uncertain in the coneixement vs. uncertain in the given. Generic characterization of the main methods of approximate reasoning.
  8. Probabilistic Reasoning.
    Probability Theory for the Treatment of Uncertainty. Bayesian Networks. Inference in Bayesian Networks. Variants: certainty factors, evidence.
  9. Fuzzy Reasoning,
    Theory of Possibility and Fuzzy Logic. Uncertainty and Ambiguity. Inference in Fuzzy Logic.
  10. Other forms of inference:
    Induction, abduction, analogy, case-based reasoning. Model and execution cycle of case-based reasoning systems. Internal organization of the Case Base.
  11. Semantic Knowledge Modeling
    Semantic Networks and Frame Networks. Description Logics.
  12. Ontologies and reasoning.
    Concept of Ontology. Forms of reasoning in ontologies.
  13. Knowledge-Based systems and their engineering
    Knowledge engineering. Phases of the development of a knowledge-based system.

Activities

Activity Evaluation act


Intelligence, Knowledge, Reason, Reasoning and Computing

Presentation of the fundamental concepts that link intelligence with reasoning, reasoning with knowledge and this with its representation. Reasoning as manipulation of representations. Reasoning as a calculation.
Objectives: 1 2 3
Contents:
Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Propositional Logic.


  • Problems: Basic problems of fundamental inference schemes
Objectives: 1 2
Contents:
Theory
4h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

First-Order Logic.

Hay que entender y practicar las diveres formas y métodos de inferencia lógica así como incluir los límites expresivos de este lenguaje, que resulta una extensión de lo que permite la lógica proposicional y al mismo tiempo nos permite entender su relación con las propiedades que interesan desde el punto de vista de su realización por medios computacionales. Esto permite entender las bases de la programación lógica.
Objectives: 1 2 3 4
Contents:
Theory
6h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
7h

Logic Programming

It is necessary to understand the language of logical programming as a computational transposition of the inference mechanisms of first-order logic and at the same time to understand its differences. It will be practiced intensively in the laboratory with exercises of increasing difficulty that will serve to prepare the specific examination of logical programming.
Objectives: 1 2 3 4
Contents:
Theory
4h
Problems
0h
Laboratory
7h
Guided learning
0h
Autonomous learning
17h

Knowledge-Based Systems. Basic Architectures.

The role of knowledge in general (beyond logical representations) in systems that use knowledge to make decisions and see the differences and similarities with what can be represented and with what can be inferred in logical systems needs to be understood and studied.
Objectives: 4 5
Contents:
Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
1h

Probabilistic Reasoning.

You need to understand and practice with various exercises how to work with knowledge that is uncertain. Probabilistic formalizations allow us to face the very common situation in which there is uncertainty in the reality on which we must reason. In the laboratory we will practice in the representation of probabilistic knowledge and we will use environments that admit it, carrying out increasingly complex exercises that will allow us to prepare the corresponding exam.
Objectives: 2 3 4 5 6
Contents:
Theory
2h
Problems
1h
Laboratory
1h
Guided learning
0h
Autonomous learning
4h

Fuzzy Reasoning.

We will see that probability theory has some difficulty in representing situations that are imprecise or ambiguous or that combine these properties with uncertainty. The Theory of Possibility must be understood as related to an appropriate representation for these conditions: fuzzy sets. In the laboratory we will practice with increasingly complex diffuse representation and reasoning exercises to confront the corresponding exam.
Objectives: 2 3 4 5 6
Contents:
Theory
2h
Problems
1h
Laboratory
1h
Guided learning
0h
Autonomous learning
4h

Other inferences forms

It must be understood that deduction is a form of reasoning among many others that we have developed. We will understand and practice through exercises the inductive inference, the basis of the experimental sciences and, in general, of all those that generalize from observations (and the corresponding data); abductive inference as a generative inference and case-based analogy or reasoning as a type of reasoning where the similarity between the components and structure of a situation sets in motion a reasoning that has useful and practical consequences. The various exercises will allow us to strengthen the knowledge of the possibilities and limitations of these types of knowledge, always comparing them with the properties of standard logic.
Objectives: 1 2 3 4 5 6
Contents:
Theory
2h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Semantic Knowledge Modeling. Ontologies.

Ontologies are formalisms based on hierarchies of concepts and relationships. We will study the main realizations and formalisms and in the laboratory we will work with ontology development environments. Students should not only attend lessons, but also do exercises on the use of ontologies and discuss with the teacher and other students when it is best to use each technique. In the lab students will apply what they have learned to a problem.
Objectives: 3 4 5 6
Contents:
Theory
2h
Problems
1h
Laboratory
2h
Guided learning
0h
Autonomous learning
7h

Inference with ontologies. Description Logic.

We will see the relationship between ontologies and logic through a specific type of logic: the logic of descriptions. We will practice with these concepts in the laboratory.
Objectives: 1 2 3 4 5 6
Contents:
Theory
4h
Problems
1h
Laboratory
3h
Guided learning
0h
Autonomous learning
6h

Knowledge-Based Systems and their engineering.

We will learn how to build a knowledge-based system from conception to implementation with special emphasis on knowledge elicitation techniques and the importance of validating them. We will study the relationship of the necessary methods in each phase of the construction of the SBC and compare them with other alternative and complementary methods of building knowledge bases from data. You will need to design a small knowledge-based system.
Objectives: 4 5 6
Contents:
Theory
2h
Problems
2h
Laboratory
0h
Guided learning
3h
Autonomous learning
6h

Approximate Rasoning Test.



Week: 8
Type: problems exam
Theory
0h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Test on Ontologies and Description Logics


Objectives: 1 2 3 4 5 6
Week: 11
Type: problems exam
Theory
0h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Test of Logic Programming

A test using a logical programming environment which consists of logical programming exercises to check the level of achievement in this approach to programming which is related to various forms of reasoning.
Objectives: 1 2 3
Week: 6
Type: lab exam
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
10h

Final Exam

Theoretical-practical exercise that covers the topics of the course.
Objectives: 1 2 3 4 5 6
Week: 18 (Outside class hours)
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
3h
Autonomous learning
0h

Teaching methodology

The teaching methodology will consist of the exposition of the theory in theory classes and the application of the concepts in the problem and laboratory classes and to small projects to be worked in group.

Evaluation methodology

The evaluation is based on several test of the thematic blocks that made up the course and an final examen as well as an evaluation of the assignments of the course in problems and laboratory classes. The final examination tests the knowledge about the theoretical aspects of the course and of the methodology acquired by the students during the course. The grading of the course assignments will be based on the presentations of small problems proposed during the course.

The final grade will be calculated as follows:

0.30 * note control logic programming + 0.10+ note control Approximate Reasoning + 0.20*control Ontologies and Logic of descriptions + 0,1 lab and problems + 0.35 Final Exam.


Assessment of competencies

The assessment of teamwork competence is based on the work done during the internship work.

Competency assessment. Solvent use of information resources is based on both internship work and problem-solving exercises and laboratories.

Bibliography

Basic:

Complementary:

  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference - Pearl, Judea, Morgan Kaufmann, Publishers , 1988. ISBN: 1558604790

Previous capacities

The usual ones in a first university course with special relevance of the contents of science and mathematics,

Addendum

Contents

No es preveu cap canvi de continguts.

Teaching methodology

La metodologia docent s'adequarà a la situació del moment. Es faran de forma no presencial amb Meet les activitats que no es puguin dur a terme de forma presencial.

Evaluation methodology

Està previst fer els exàmens de forma presencial. Si la situació no ho permetès, es farien de forma no presencial amb Meet i/o la plataforma Atenea, o qualsevol medi similar facilitat per la UPC.

Contingency plan

Si no fos possible dur a terme activitats presencials, es farien tant les classses de teoria, com de problemes de forma no presencial amb Meet, així com tots els actes d'avaluació de forma no presencial utilitzant Meet i/o la plataforma Atenea.