It is highly recommended to have previously studied the subjects of Programming Methodology and Data Structure, both in the field of programming and also as a subject from which it starts the third year of the degree course of Intelligent Systems. The subject of "knowledge based systems" is framed in the specific technology of Computing and therefore it is closely related to the subjects dedicated to the study of computer sciences and to intelligent systems or artificial intelligence. That is why it is highly recommended to have previously studied the subjects of Programming Methodology and Data Structure, as well as Logic, both in the field of programming and logical programming, and also, and as initial subject of the scope of the Artificial Intelligence, Intelligent Systems of third course of degree. In any case, it is advisable to have completed the basic training modules and the common module of the IT branch.
There are very complex problems in the field of application of the construction of Software Systems where the step-by-step description of the solutions to them is unapproachable, either by computer time or by memory space when not in both directions.
It is in this environment where all available expert knowledge must be incorporated to solve complex problems as an expert in the domain in question would do.
The subject is part of the Computer Intensification, where all the specific competences are developed in subjects of Intelligent Systems, Data Mining, Intelligent Agents and Fundamentals of Computing.
To get an idea of what we are talking about, imagine for a moment how a mining engineer decides the drilling of new oilfields, there are so many variables to take into account and possible scenarios to analyze that it is practically impossible to tackle all at once. This expert, in this field, will follow guidelines/rules that will allow him, with the accumulated experience, decide at any time the most probable scenarios to take into account and the variables to consider in their evaluations, greatly reducing the complexity of the problem and providing a cost-effective solution, in our case, decide whether to invest in a new drilling (with the consequent execution costs). In this course, paradigms that attempt to capture this type of knowledge will be approached in order to be able to reason and solve problems of this type with reasonable time and efficiency.
In the case of perforations, a model could be constructed that, faced with environmental and/or geological variables, etc., will decide whether the perforation is worthwhile or not. As main justification in the curriculum we could summarize it in that there are many real problems where you must know the rules or guidelines of how to intelligently solve this type of problems experts in their field, for us to implement appropriate data structures and programs to represent and manage this expert knowledge and thus provide an appropriate solution to these problems. This subject is closely related to others in the curriculum, perhaps the most related is Intelligent Systems, as the base subject of this, in addition to the entire programming module, data structures, programming methodology.
But in addition this subject will help to obtain the competences of others like Mutiagent Systems, when intelligent agents are designed; Algorithm Design, there are programming techniques and more sophisticated data structures that are used in both disciplines. In general, all subjects of specific computer technology are related, although those discussed above may be more closely related.
Course competences | |
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Code | Description |
CM04 | Ability to know the fundamentals, paradigms, and techniques of intelligent systems, and analyse, design, and build systems, services, and digital, applications which could use such techniques in any application context. |
CM05 | Ability to acquire, formalise, and represent human knowledge in a computable form for the solution of problems throughout a digital system in any application context, especially the one linked to computational aspects, perception, and behaviour in intelligent frames. |
INS01 | Analysis, synthesis, and assessment skills. |
INS04 | Problem solving skills by the application of engineering techniques. |
INS05 | Argumentative skills to logically justify and explain decisions and opinions. |
PER02 | Ability to work in multidisciplinary teams. |
PER04 | Interpersonal relationship skills. |
PER05 | Acknowledgement of human diversity, equal rights, and cultural variety. |
SIS01 | Critical thinking. |
SIS03 | Autonomous learning. |
SIS09 | Care for quality. |
UCLM03 | Accurate speaking and writing skills. |
Course learning outcomes | |
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Description | |
Understanding of the paradigms of knowledge representation and inference that allow designing and implementing knowledge-based systems. | |
Additional outcomes | |
Description | |
PRACTICES
In the practices, throughout the course, a prototype of Intelligent System will be developed (Based on rules, using CLIPS). The student will propose the domain (linked to his/her work and/or research interests) or will choose it from a list of proposals.
Practices will be carried out on the CLIPS system.
The following deliveries on the Project are foreseen:
1. Introduction. Identification of the problem: Document of objectives, scope and annexes. Slagel Viability Test. State of the Art.
2. Conceptualization. Conceptualization document: Process and knowledge maps, glossaries, tables, gratings, pseudocode. Demonstration Prototype.
3. Representation of knowledge. CLIPS Code. Interface. Definitive prototype. Evaluation of the system.
Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
Individual tutoring sessions [ON-SITE] | Group tutoring sessions | CM04 CM05 INS05 SIS01 SIS09 UCLM03 | 0.18 | 4.5 | N | N | Individual or small group tutoring in lecturer¿s office, classroom or laboratory (TUT) | |
Study and Exam Preparation [OFF-SITE] | Self-study | CM04 CM05 INS01 SIS01 SIS03 SIS09 | 1.8 | 45 | N | N | Self-study (EST) | |
Other off-site activity [OFF-SITE] | Practical or hands-on activities | CM04 CM05 INS04 PER02 PER04 PER05 SIS03 SIS09 | 0.9 | 22.5 | N | N | Lab practical preparation (PLAB) | |
Problem solving and/or case studies [ON-SITE] | Problem solving and exercises | CM04 CM05 INS01 INS04 PER02 PER04 PER05 SIS01 SIS09 | 0.6 | 15 | Y | N | Worked example problems and cases resolution by the lecturer and the students (PRO) | |
Writing of reports or projects [OFF-SITE] | Self-study | CM04 CM05 INS01 INS04 INS05 PER02 PER04 PER05 SIS01 SIS03 | 0.9 | 22.5 | Y | N | Preparation of essays on topics proposed by lecturer (RES) | |
Laboratory practice or sessions [ON-SITE] | Practical or hands-on activities | CM04 CM05 INS04 PER02 PER04 PER05 SIS03 SIS09 | 0.72 | 18 | Y | Y | Realization of practicals in laboratory /computing room (LAB) | |
Progress test [ON-SITE] | Assessment tests | CM04 CM05 INS01 INS04 INS05 PER02 SIS01 SIS09 UCLM03 | 0.1 | 2.5 | Y | N | Progress test 1 of the first third of the syllabus of the subject (EVA) | |
Progress test [ON-SITE] | Assessment tests | CM04 CM05 INS01 INS04 INS05 PER02 SIS01 SIS09 UCLM03 | 0.1 | 2.5 | Y | N | Progress test 2 of the two first thirds of the syllabus of the subject (EVA) | |
Progress test [ON-SITE] | Assessment tests | CM04 CM05 INS01 INS04 INS05 PER02 SIS01 SIS09 UCLM03 | 0.1 | 2.5 | Y | N | Progress test 3 of the complete syllabus of the subject (EVA) | |
Class Attendance (theory) [ON-SITE] | Lectures | CM04 CM05 | 0.6 | 15 | N | N | Teaching of the subject matter by lecturer (MAG) | |
Total: | 6 | 150 | ||||||
Total credits of in-class work: 2.4 | Total class time hours: 60 | |||||||
Total credits of out of class work: 3.6 | Total hours of out of class work: 90 |
As: Assessable training activity Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).
Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
Progress Tests | 7.50% | 0.00% | Progress test 1. Non-compulsory activity that can be retaken (rescheduling). To be carried out at the end of the first third of the teaching period. |
Progress Tests | 15.00% | 0.00% | Progress test 2. Non-compulsory activity that can be retaken. To be carried out at the end of the second third of the teaching period. |
Progress Tests | 27.50% | 0.00% | Progress test 3. Non-compulsory activity that can be retaken. To be carried out during the non-teaching period. |
Theoretical papers assessment | 15.00% | 15.00% | Non-compulsory activity that can be retaken. To be carried out before end of teaching period. |
Laboratory sessions | 25.00% | 25.00% | Compulsory activity that can be retaken. To be carried out during lab sessions. |
Oral presentations assessment | 10.00% | 10.00% | Non-compulsory activity that can be retaken. To be carried out during the theory/lab sessions. |
Final test | 0.00% | 50.00% | Compulsory and recoverable activity to be carried out on the date scheduled for the final ordinary examination. |
Total: | 100.00% | 100.00% |
Not related to the syllabus/contents | |
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Hours | hours |
Unit 1 (de 3): Introduction and basic concepts of Knowledge Engineering. | |
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Activities | Hours |
Individual tutoring sessions [PRESENCIAL][Group tutoring sessions] | 1.5 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 15 |
Other off-site activity [AUTÓNOMA][Practical or hands-on activities] | 7.5 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 5 |
Writing of reports or projects [AUTÓNOMA][Self-study] | 7.5 |
Laboratory practice or sessions [PRESENCIAL][Practical or hands-on activities] | 6 |
Progress test [PRESENCIAL][Assessment tests] | .5 |
Progress test [PRESENCIAL][Assessment tests] | .5 |
Progress test [PRESENCIAL][Assessment tests] | .5 |
Unit 2 (de 3): Knowledge Acquisition and Conceptualization | |
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Activities | Hours |
Individual tutoring sessions [PRESENCIAL][Group tutoring sessions] | 1.5 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 15 |
Other off-site activity [AUTÓNOMA][Practical or hands-on activities] | 7.5 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 5 |
Writing of reports or projects [AUTÓNOMA][Self-study] | 7.5 |
Laboratory practice or sessions [PRESENCIAL][Practical or hands-on activities] | 6 |
Progress test [PRESENCIAL][Assessment tests] | 1 |
Progress test [PRESENCIAL][Assessment tests] | 1 |
Progress test [PRESENCIAL][Assessment tests] | 1 |
Unit 3 (de 3): Representation of knowledge. | |
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Activities | Hours |
Individual tutoring sessions [PRESENCIAL][Group tutoring sessions] | 1.5 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 15 |
Other off-site activity [AUTÓNOMA][Practical or hands-on activities] | 7.5 |
Problem solving and/or case studies [PRESENCIAL][Problem solving and exercises] | 5 |
Writing of reports or projects [AUTÓNOMA][Self-study] | 7.5 |
Laboratory practice or sessions [PRESENCIAL][Practical or hands-on activities] | 6 |
Progress test [PRESENCIAL][Assessment tests] | 1 |
Progress test [PRESENCIAL][Assessment tests] | 1 |
Progress test [PRESENCIAL][Assessment tests] | 1 |
Global activity | |
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Activities | hours |
General comments about the planning: | The subject has 3 sessions/week of 1,5 hours. |