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E6. Develop video games in high-level programming languages in graphics engines based on specifications.
T1. Communicate in a third language, preferably English, with an appropriate level of oral and written communication and in accordance with the needs of graduates.
T2. Work as a member of an interdisciplinary team either as an additional member or performing management tasks in order to contribute to developing projects with pragmatism and a sense of responsibility, making commitments and taking into account available resources.
This subject aims to introduce degree students to the field of artificial intelligence, and specifically to computational behaviour, showing them the application of some of its techniques in the construction of video games. Issues such as movement-based behaviors, including wayfinding, and a small range of decision-making mechanisms of a reactive nature are seen. Decision-making mechanisms of a deliberative nature ("minimaxing" and planning) are also addressed, but in less depth. The theoretical aspects are worked on, in an expository way, and their subsequent practical application, aimed at the resolution, often guided, of small problems. The class sessions combine both aspects in order to achieve a good balance between them. The practices (compulsory) and exercises in class and at home make up the evaluation model of the subject.
This subject should be taken once the entire first year and the "programming in interpreted languages" subject of the second year have been passed. It would also be advisable to have passed the subject "Development of 2D games".
At the end of the course students must be able to:
E6.1 Design the software architecture of a video game according to specifications
E6.4 Classify and describe the main behaviors of artificial intelligence in video games and exemplify them with references in the video games market.
E6.6 Develop 2D and 3D video games (or parts thereof) in high-level languages on platforms and engines intended for this purpose.
The subject mostly uses these two methodologies: the master class and problem solving.
All the theoretical concepts of the subject will be exposed in theory classes (large groups) of a masterful nature. The “non-masterful” part of the sessions will be devoted to problem solving and short activities. Laboratory sessions with an eminently practical nature will also be scheduled.
Students must attend all classes with a laptop with the ability to run the appropriate software for the subject. Teachers will report on what this software is and how it can be obtained.
Topic 1. Introduction. AI and AI for games. Computational behaviors
Topic 2. Motion control: "Steering behaviors"
2.1 Basic and derived behaviors: seek, arrive, wander, velocity matching,...
2.2 Combination of behaviors. Flocking
Topic 3. Pathfinding: "Pathfinding"
3.1 Representation of space: graphs
3.2 The A star algorithm
Item 4. Decision making
4.1 State machines
4.2 Behavioral trees
4.3 Zero-sum turn-based games: the minimax algorithm
4.4 Goal-oriented behaviors
In order to gather evidence of the achievement of the expected learning outcomes, the following evaluative activities will be carried out:
A1. Practice of motor behaviors and state machines (Laboratory practice / group work evidence of learning outcomes E6.1 and E6.6)
A2. Pathfinding practice (Laboratory practice / group work evidence of learning outcomes E6.1 and E6.6)
A3. Practice 3 with variable subject (Laboratory practice / group work evidence of learning outcomes E6.1 and E6.6)
The content of the practices will make special emphasis in what indicates the title although will be able to contain other own contents of the asignatura and his area.
A4. Final Exam (evidence of learning outcomes E6.1, E6.4 and E6.6)
The student will have to show his knowledge with regard to the theoretical appearances of the asignatura and do small developments of practical character.
General criteria of the activities
- The teacher will present a statement for each activity and the evaluation and / or rubric criteria
- The teacher will inform of the dates and the format of delivery of the activity
The grade of each student will be calculated following the following percentages:
A (1,2,3). Laboratory practices / group work: 50% (1/3 50% each)
A4. Final Exam: 50%
Final Note = A (1,2,3) · 0.5 + A4 · 0.5
- A4> = 5 is required to pass the subject. If this grade does not reach 5 then she herself will be the final grade.
- An activity not delivered or delivered late and without justification (court summons or medical matter) counts as a 0.
- It is the student's responsibility to avoid plagiarism in all its forms. In the case of detecting plagiarism, regardless of its scope, in any assessment activity (including practices), article 8 of the assessment regulations will apply, which entails the automatic suspension of the subject without the possibility of recovery. In addition, the professor will communicate the situation to the Head of Studies so that he can take applicable measures in terms of disciplinary regime. In the context of this subject, plagiarism also means using and/or adapting code that has not been developed entirely individually (or within the group in the case of group activities). Facilitating code that results in plagiarism is also a form of plagiarism and will be treated in the same way. In summary, we can say that assessment activities must be solved in a strictly non-collaborative way (in the case of group activities, collaboration cannot go beyond the group itself).
- It is necessary to obtain a mark> = 5 in the final exam of recovery to pass the asignatura.
- The mark of the recovery exam will be applied to the A4 activity (and the formula Final Grade = A (1,2,3) · 0.5 + A4 · 0.5 will be applied again)
- In case of passing the recovery (A (1,2,3) · 0.5 + A4 · 0.5> = 5) the maximum final mark of the subject will be 5
Millington, I. (2019). AI for games. Boca Raton, FL: CRC Press, Taylor & Francis Group.
Buckland, M. (2009). Programming game AI by example. Plano, TX: Wordware Publ.