Knowledge-Based Systems

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
Web
https://sites.google.com/upc.edu/sbcgia-sbc/inicio
Introduction and work on the cognitive paradigm and its organization. In particular everything that is related to the different variants of the systems that are based on the representation and manipulation of knowledge, including the various forms of implementation and their regimes of operation. Development methodologies are also discussed: elicitation of knowledge, representation and selection of reasoning strategies.

Teachers

Person in charge

Others

Weekly hours

Theory
2
Problems
0
Laboratory
2
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.

    • Basic

  • CB1 - That students have demonstrated to possess and understand knowledge in an area of ??study that starts from the base of general secondary education, and is usually found at a level that, although supported by advanced textbooks, also includes some aspects that imply Knowledge from the vanguard of their field of study.
  • CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.

    • 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 a knowledge-based system, its relationship with cognition and with the representation of knowledge
      Related competences: CG2, CG4, CT5, CB1, CB2, CB4, CE15,
    2. To know and understand the different architectures of knowledge-based systems
      Related competences: CG2, CG4, CT5, CB2, CB4, CE15,
    3. To know and understand the various forms of knowledge representation, reasoning and to practice their design and implementation implementation in the various architectures of knowledge-based systems
      Related competences: CG5, CT4, CE02, CE18,

    Contents

    1. Introduction to Knowledge-Based Systems Systems based on knowledge. Characteristics. components Problems solvable through SBCs.
      A thorough exploration of the different types of Knowledge-Based Systems, their components and applications.
    2. Reasoning Based on Semantic/Procedural Knowledge
      Types of Knowledge. Knowledge representation schemes.
      Semantic Knowledge: Semantic Networks. Logical description. Networks of Frames. Ontologies. Ontological reasoning
      Procedural knowledge. Rule-based reasoning systems. Fact bases, knowledge bases, inference engine, meta-knowledge, ...
      Knowledge engineering. Phases of knowledge engineering. Knowledge management.
      SBCs with more than one Knowledge Representation Scheme. Meta-knowledge, combination of results.
    3. Reasoning Based on Experience
      Reasoning Based on Experience
      Episodic knowledge: Reasoning based on experience. Modeling experience with Cases, Case-Based Reasoning (CBR). Fundamentals of CBR: Introduction, Cognitive theory, Basic cycle of reasoning. Academic Examples/Demonstrators.
      Components of a CBR system: Structure of the cases. Organization of the Library/Case Base. Recovery of cases. Adaptation of cases. Case evaluation. Case study.
      Application of a CBR system to a real case. Important aspects in the development of CBR systems.
      Reflexive Reasoning in CBR systems. Maintenance of a CBR system. Industrial applications of CBR systems. CBR system development tools
      Evaluation of CBR systems. Advanced topics in CBR: Temporal CBR, Spatial CBR, Hybrid CBR Systems
    4. Collaborative Reasoning
      Collaborative Reasoning
      Introduction: Intelligent Decision Support Systems (IDSS), Recommender Systems. General architecture of a recommender system.
      Classification of Recommender Systems. Basic Recommendation techniques: Collaborative Filtering, Content-based Filtering.
      Other Recommendation techniques: knowledge-based (case-based, constraint-based), community-based, demographic-based, hybrid approaches
      KPIs in Recommendation Systems: performance, competence. Evaluation of the quality of a Recommendation System: quantitative measures, qualitative measures
      Applications of Recommendation Systems (Amazon, Netflix, ...). Future trends in Recommendation Systems

    Activities

    Activity Evaluation act


    Introduction to Knowledge-Based Systems

    Knowledge-Based Systems. Characteristics. components Problems solvable through SBCs.
    Objectives: 1 2 3
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    4h

    Reasoning Based on Semantic and Procedural Knowledge

    Reasoning Based on Semantic and Procedural Knowledge
    Objectives: 1 2 3
    Contents:
    Theory
    10h
    Problems
    0h
    Laboratory
    10h
    Guided learning
    0h
    Autonomous learning
    10h

    Reasoning Based on Experience

    Reasoning Based on Experience
    Objectives: 3
    Contents:
    Theory
    8h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Collaborative Reasoning

    Collaborative Reasoning
    Objectives: 3
    Contents:
    Theory
    8h
    Problems
    0h
    Laboratory
    6h
    Guided learning
    0h
    Autonomous learning
    8h

    Reasoning practice with ontologies and rule systems control

    Reasoning practice with ontologies and rule systems control
    Objectives: 1 2 3
    Week: 7 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    CBR practical project control.

    CBR practical project control.

    Week: 12 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Reasoning with Ontologies and rule systems practical work

    Reasoning with Ontologies and rule systems practical wo

    Theory
    0h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    30h

    CBR practical project

    CBR practical project

    Theory
    0h
    Problems
    0h
    Laboratory
    6h
    Guided learning
    0h
    Autonomous learning
    30h

    Teaching methodology

    The classes are divided into theory, problem and laboratory sessions.

    In the theory sessions, knowledge of the subject will be developed, interspersed with the presentation of new theoretical material with examples and interaction with the students in order to discuss the concepts.

    The problem classes will allow you to deepen the techniques and algorithms explained in the theory sessions. Student participation will be encouraged in order to comment on possible alternatives.

    In the laboratory classes, small practices will be developed using tools and languages specific to Artificial Intelligence that will allow practicing and reinforcing the knowledge of the theory classes.

    Evaluation methodology

    Assessment will be based on practicals only

    NP1: note of the first practice
    NP2: note of the second practice
    NFinal = 0.5*NP1+0.5*NP2



    Assessment of skills

    The assessment of teamwork competence (CT4) is based on the work done during the laboratory practices. The grade A B C D is calculated from a detailed rubric that will be given to students at the beginning of the year.
    The evaluation of the competence of the information resources (CT5). it is based on the work done during the internship. The grade A B C D is calculated from a detailed rubric that will be given to students at the beginning of the year.
    Weight of transversal skills in the evaluation of the specific part of the subject
    10% - That students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the development and defense of arguments and the resolution of problems within their area of expertise study
    10% - Teamwork. Be able to work as a member of an interdisciplinary team, either as another member or performing management tasks, in order to contribute to developing projects with pragmatism and a sense of responsibility, making commitments taking into account the available resources.

    Bibliography

    Basic

    Previous capacities

    Knowledge and Automatic Reasoning. (1rst Term, of the 1st Year)