Checking date: 04/06/2021

Course: 2021/2022

Knowledge Engineering
Study: Dual Bachelor in Computer Science and Engineering, and Business Administration (233)

Coordinating teacher: FERNANDEZ ARREGUI, SUSANA

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Algorithms and Data Structures, Artificial Intelligence, Logic
ABET competences: - Problem solving, both stand-alone and working in teams (PO c, e) - To work in a team both for the analysis and design of computer solutions based on knowledge engineering (PO d) - Ability to analyze and synthesize (PO b) - Organization and planning (PO d) - Information management (both acquiring and analyzing information) (PO b) - Decision making (PO k) - Encouragement for improving quality (PO e, i) - Spoken and written communication (PO g) - Critical reasoning (PO b, c, e, k) - Basic and fundamental knowledge of Artificial Intelligence and Knowledge Engineering (PO i, k) - Ability to interpret and understand functional specifications for the development of knowledge engineering systems (PO c) - To analyze and design computer applications based on Knowledge Engineering (PO c) EUR-ACE competences: - Ability to learn the basics, paradigms and techniques of intelligent systems and analyze, design and build systems, services and applications that use these techniques in any scope (CECC4): 2 ECTS - Ability to acquire, obtain, formalize and represent human knowledge in a computable way to solve problems through a computer system at any scope, particularly those related to aspects of computing, perception and action in intelligent environments (CECC5): 4 ECTS EUR-ACE learning results: - Understanding of the different methods and the ability to use them (RA3.2) - The ability to combine theory and practice to solve engineering problems (RA5.2) The students practice the following soft-skills: - Communication : written, listening - Flexibility : willing to change, accepts new things - Responsibility: gets the job done, self-disciplined - Teamwork : cooperative, gets along with others, collaborative
Skills and learning outcomes
Description of contents: programme
1. Introduction to Knowledge Engineering 1.1. Goals of Knowledge Engineering 1.2. Types of Knowledge Engineering systems 2. Development stages 2.1. Knowledge acquisition 2.2. Conceptualization 2.3. Formalization 2.4. Development, implementation and validation 3. Knowledge based systems 3.2. Production systems 3.1. Planning-based systems 4. Analysis, design and implementation processes of computer systems based on knowledge management 4.1 Resolution of specific problems using knowledge based systems
Learning activities and methodology
ABET: Theoretical lectures (1 credit ECTS) - Mainly oriented to the acquisition of the basic underlying concepts, their relationships, the available techniques and to discuss how to analyze and synthesize knowledge (PO b, e, k) Practices in groups (2 credits ECTS) - Mainly oriented to those competences related to strength the ability to work in a team, problem solving, work decomposition and organization, and public oral presentations and written communications (PO d, g) Individual work (3 credits ECTS) - Mainly oriented to those competences related to planning, analyzing, synthesizing, critical reasoning and familiarization with the various concepts (PO b, c, e, k) EUR-ACE: - Theoretical lectures: - Mainly oriented to the acquisition of the theoretical knowledge of the competences CECC4 and CECC5 - Practices in groups - Mainly oriented to the acquisition of the practical skills related to the competences CECC4 and CECC5 - Individual work to study and complete the practices Students have to perform the following work in pairs to acquire the EUR-ACE learning results RA3.2 and RA5.2: - Two mandatory practices consisting in solving the same problem using two different techniques of Artificial Intelligence: Production Systems and Automated Planning. It includes the design of the programs, the knowledge acquisition, the implementation and testing. Deliverables are the source code and the documentation explaining the programs
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • Nils J. Nilsson. Artificial Intelligence: A New Synthesis. Morgan Kaufmann.
  • Schreiber, Guus. Knowledge engineering and management : the commonKADS methodology. MIT Press.
  • Stuart Russell, Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson / Prentice-Hall.

The course syllabus may change due academic events or other reasons.

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