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
The course is an introduction to the basic concepts of reasoning and its automation.

It begins with a historical introduction to the various paradigms of AI in order to contextualize the role of knowledge in intelligence, as well as that of reasoning. It is argued that the manipulation of knowledge representations can be the basis for the automation of reasoning which is one of the ways of obtaining intelligent behaviours in artificial agents.

Logic is characterized as a basic formalism to represent knowledge. Starting from the logical characterization of various forms of inference, the need for learning is motivated as a crucial means for an intelligent agent to act as such.

Teachers

Person in charge

  • Ramon Sangüesa Sole ( )

Others

  • Caroline König ( )

Weekly hours

Theory
2
Problems
1
Laboratory
1
Guided learning
0
Autonomous learning
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 know now how to apply logical foundations to the increasing number of applications of reasoning methods in computing.
    Related competences: CG2, CG4, CE02, CE15,
  3. To be able to analyze the knowledge that is necessary to solve a problem.
    Related competences: CG2, CG4, CG5, CT5, CE15, CE18,
  4. 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,
  5. 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,
  6. 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,

Contents

  1. Introduction: Intelligence, Knowledge, Reason, Reasoning and Computing.
    Brief history of AI and its paradigms. 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. The concept of rational agent.
  2. Reasoning and logic
    Logic as a representation of knowledge. Logic with a reasoning mechanism. deduction Properties of logical systems.
  3. 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.
  4. Logic Programming.
    Introduction to logical programming answer calculation, resolution strategies, backtracking management.
  5. Other forms of inference:
    Non-monotonic reasoning. induction abduction analogy Protopic and taxonomic reasoning. learning
  6. Semantic Knowledge Modeling
    Ontologies. Description Logics.

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 6 2
Contents:
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Propositional Logic.


  • Problems: Basic problems of fundamental inference schemes
Objectives: 1 6
Contents:
Theory
6h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

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 6 2 3
Contents:
Theory
8h
Problems
7h
Laboratory
0h
Guided learning
0h
Autonomous learning
16h

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 6 2 3
Contents:
Theory
8h
Problems
1h
Laboratory
10h
Guided learning
0h
Autonomous learning
21h

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 6 2 3 4 5
Contents:
Theory
2h
Problems
1h
Laboratory
1h
Guided learning
0h
Autonomous learning
4h

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: 2 3 4 5
Contents:
Theory
2h
Problems
2h
Laboratory
4h
Guided learning
0h
Autonomous learning
7h

Final Exam

Theoretical-practical exercise that covers the topics of the course.
Objectives: 1 6 2 3 4 5
Week: 15 (Outside class hours)
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Practical project of Logic Programming

Team project using a logic programming environment that focuses on solving a limited problem by applying knowledge about logic programming and reasoning strategies
Objectives: 1 6 2
Week: 6 (Outside class hours)
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
18h

Midterm Exam

Evaluation of the content, techniques and methods covered up to the time of the exam. Theoretical-practical exam with questions about readings, concepts and exercises.
Objectives: 1 6 2
Week: 6
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

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* Logic Programming Project + 0.3* Midterm Exam+ 0.40 * Final Exam


Assessment of competencies

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

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

Bibliography

Basic:

Previous capacities

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