Intelligent System Project

You are here

Credits
3
Types
Elective
Requirements
This subject has not requirements

Department
CS
The aim of this course is the construction of an Intelligent System to perform a non-trivial task. The development of an Intelligent System shares many steps with the development of any software system. Nowithstanding, there are some special features like the knowledge acquisition step, task analysis, selection of Intelligent methods, integration of Intelligent techniques, etc. that are especific of Intelligent System projects. The IS project is a project like those ones that students will cope with, in their professional practice of Artificial Intelligence in any company. The project will be constructed by a team of three or four students.

Teachers

Person in charge

  • Miquel Sanchez Marre ( )

Competences

Generic Technical Competences

Generic

  • CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.

Technical Competences of each Specialization

Professional

  • CEP4 - Capability to design, write and report about computer science projects in the specific area of ??Artificial Intelligence.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

Transversal Competences

Sustainability and social commitment

  • CT2 - Capability to know and understand the complexity of economic and social typical phenomena of the welfare society; capability to relate welfare with globalization and sustainability; capability to use technique, technology, economics and sustainability in a balanced and compatible way.

Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.

Objectives

  1. The students will be able to integrate and apply several knowledge acquired in previous Master courses for the solving of complex problems using Artificial Intelligence techniques
    Related competences: CB6, CB7,
  2. Students will be able to write and communicate their technical and research work on Intelligent Systems and achievements both to a general and specialized audience.
    Related competences: CEP4, CB8,
  3. Students will acquire and learn the concepts and knowledge related to sustainability and their intrinsic relationship with Intelligent Systems.
    Related competences: CEP8, CT2,
  4. Students will consolidate teamworking abilities.
    Related competences: CT3,
  5. Students will be able to design and construct an Intelligent System to solve a non trivial problem.
    Related competences: CT7, CG1, CEP5,

Contents

  1. Introduction
    Description of the aims of the course. Description of the team works. Information about the IS project timeline. Deliverables of the IS project.
  2. Problem Analysis
    Problem Feature Analysis. Information/Data Analysis. Viability Analysis. Economical Analysis. Environmental and Sustainability Analysis.
  3. Definition of the Intelligent System project issues
    Definition of main goals of the IS project. Definition of sub-goals. Task Analysis.
  4. Development of an Intelligent System Project
    Data/Information Extraction. Data Mining & Knowledge Acquisition Process. Knowledge/Ontological Analysis. Planning and selection of Intelligent/Statistical/Mathematical Methods/Techniques. Construction of Models and implementation of Techniques. Module Integration. Validation of Models/Techniques. Comparison of Techniques. Proposed Solution.
  5. Intelligent System Project Output
    Executive Summary. Project System Documentation: User's Manual, System Manual. Project Schedule (Gantt's Chart). The Project Time Sheet.
  6. Intelligent Methods and Models
    Review of main Intelligent Methods available.
  7. Software tools
    Review of main software tools available.

Activities

Introductory Lab Session

First Lab class will focus on laboratory working teams and on giving information about the IS project. (timeline, deliverables, etc.)
Theory
0
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
2
Objectives: 4
Contents:

Lab Sessions on the analysis of the problem and the design and implementation of an Intelligent System Project

The following classes will be dedicated to providing information about the process of developing an Intelligent System and all its phases
Theory
0
Problems
0
Laboratory
4
Guided learning
0
Autonomous learning
4
  • Laboratory: Problem analysis and design and implementation of an Intelligent System Project.
Objectives: 3 5
Contents:

Laboratory sessions on the review of intelligent methods and intelligent software tools available

Theory
0
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
2
  • Laboratory: Session review of the main Artificial Intelligence models and software tools
Objectives: 1
Contents:

Laboratory sessions for tracking the project

The remaining laboratory classes (7) is devoted to oversee and guide the various Intelligent Systems projects of the different groups.
Theory
0
Problems
0
Laboratory
16
Guided learning
0
Autonomous learning
0
  • Laboratory: Lab sessions for the development of the IS project
Objectives: 5

Teaching methodology

In general there will be different kind of teaching methods:
- Expositive Lectures
- Participative Lectures
- Project Supervising Classes
- Orientation classes for Autonomous work and cooperative teamwork

Concretely:
The first class will be focused on laboratory working teams, and basic information about the project will be given.
The following classes (3-4) will be devoted to providing information about the process of developing an Intelligent System and all its phases.
The remaining laboratory classes (7) will be devoted to oversee and guide the Intelligent System projects of different groups.

Evaluation methodology

The assessment of the achievement of the objectives of the course will be made by assessing the achievements of an Intelligent System project throughout the course, which will be done working in teams of 3 or 4 students.

The final grade (NFIN) is a weighted average between the teamwork (NGRUP) assesment and the evaluation of the work of each individual student (NIND) according to the formula:

NFIN = pg * NGRUP + pi * NIND, where the percentages (pg and pi) with 0.3 <= pg <= 0.5 and 0.7>= pi >= 0.5, will be determined at the beginning of each course.

The individual grade for each student (NIND) will be obtained by observing and assessing the ongoing work and participation of each student throughout the project, according to the teacher.

The teamwork grade (NGRUP) will be a weighted average between two marks related to the midterm delivery of system analysis and design (NMIDTER), and the final presentation of the project (NFINPR). It will be computed according to the formula:

NGRUP = 0.3 * NMIDTER + 0.7 * NFINPRES

Bibliografy

Basic:

  • Intelligent Systems for Engineers and Scientists - Hopgood, Adrian A., CRC Press , 2011. ISBN: 1439821208
  • Intelligent Systems: Principles, Paradigms and Pragmatics - Schalkoff, Robert J., Jones and Bartlett Publishers , 2011. ISBN: 0-7637-8017-0
  • Artificial Intelligence: A Guide to Intelligent Systems - Negnevitsky, Michael, Addisson-Wesley , 2004. ISBN: 0-3212-0466-2

Complementary:

  • Artificial Intelligence - A Modern Approach. - Russell, Stuart and Norvig, Peter, Prentice Hall , 2010. ISBN: 0-13-207148-7

Web links

Previous capacities

The knowledge and abilities provided by the mandatory courses of the Master