106323 - ARTIFICIAL INTELLIGENCE

Subject type: Mandatory

Coordinator: Adso Fernández Baena

Trimester: Second term

Credits: 4

This subject aims to introduce undergraduate students in the field of artificial intelligence, and specifically computational behaviors, showing them the application of some of their techniques in the construction of video games. Issues such as movement-based behaviors, including path-finding, and a small range of decision-making mechanisms of a reactive nature are seen. Decision-making mechanisms of a deliberative nature (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 solving, often guided, small problems. Class sessions combine both aspects in order to achieve a good balance between them. Classroom and homework practices and exercises make up the evaluation model of the subject. 

This subject has methodological and digital resources to make possible its continuity in non-contact mode in the case of being necessary for reasons related to the Codid-19. In this way, the achievement of the same knowledge and skills that are specified in this teaching plan will be ensured. 

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.

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, often of an evaluative nature. 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 Representation of the kinematic state

2.2 Basic and derived behaviors: seek, arrive, wander, velocity matching, ...

2.3 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 Goal-oriented behaviors 

4.4 Other decision-making mechanisms

With the aim of collecting evidence of the achievement of the expected learning outcomes, the following activities of an evaluative nature will be carried out (related to all the common competences):

A1. Laboratory practices: Practices 1, 2 and 3 (evidence of learning outcomes E6.1 and E6.4)

Directed practices performed in asynchronous time

A2. Class exercises: short exercises  (evidence of learning outcomes E6.1 and E6.4,)

These are different exercises, problems and questionnaires distributed during the course

A3. Jobs: home jobs (evidence of learning outcomes E6.1 and E6.4,)

These are different exercises distributed during the course 

General criteria of the activities

- The teacher will present a statement for each statement and the evaluation and / or rubric criteria

- The teacher will inform of the dates and the format of delivery of the activity

- One-on-one activities presuppose the student's commitment to carry them out individually and without any collaboration with other people. All activities in which the student does not comply with this commitment to individuality will be considered suspended (grade 0), regardless of their role (sender or receiver) and without this excluding the possible application of other sanctions in accordance. with the current Disciplinary Regime.

- Likewise, the activities to be carried out in groups presuppose the commitment on the part of the students who make it up to carry them out within the group and without any kind of collaboration with other groups or people who are alien (group individuality). All activities in which the group has not respected this commitment regardless of its role (sender or receiver) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime will be considered suspended (rating 0).

- In the particular case of internships, when in any of them the commitment of individuality is not respected and / or fraudulent means are used in its realization, the qualification of practices (A1) will be, for all the members of the group, of 0 points (A1 = 0) independently of the qualification of the other practices and without this exclude the possible application of other sanctions in agreement with the valid Disciplinary Regime.

- Any undelivered activity will be considered scored with zero points.

- It is optional for teachers to accept or not deliveries outside the deadlines indicated. In the event that these late deliveries are accepted, it is up to the teacher to decide whether to apply a penalty and the amount thereof.

In the 20/21 academic year and due to the expected (and other possible) restrictions related to COVID-19, activities A2 and A3 will not be evaluative (they will not be taken into account for the calculation of the final grade). For the purposes of the evaluation, they will be replaced by a final test.

The grade of each student will be calculated following the following percentages:

A1. Laboratory practices: Practices 1, 2 and 3: 50%

A2. Class exercises: short exercises: 20%

A3. Jobs: home jobs: 30%

Final Note = A1 0.5 + A2 0.2 + A3 0.3

Considerations:

- It is necessary that A2 0.4 + A3 0.6> = 5 to pass the subject. If this grade does not reach 5 then she herself will be the final grade. 

- Students with a Final Grade <5 will be able to take a resit exam. The maximum final grade in case of having to take this exam will be 5.

- 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 prevent plagiarism in all its forms. In the case of detecting a plagiarism, regardless of its scope, in any activity, it will correspond to have a note of 0. In addition, the professor will communicate to the Head of Studies the situation so that it takes applicable measures in the matter of regime. sanctioning. 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 the code that leads to plagiarism is also a form of plagiarism and will be treated the same way. In summary, we can say that the evaluation activities must be solved in a strictly non-collaborative way (in the case of group activities, collaboration cannot transcend the heart of the group). 

Recovery

- The part of practices of the subject (qualification A1) is NOT recoverable.

- Students with a Final Grade <5 will be able to take a resit exam. The grade for this exam will replace grades A2 and A3. The maximum final grade in case of having to take this exam will be 5.

- Only those students who have obtained a grade other than "not presented" in the ordinary assessment will be able to take the resit exam.

The course 

In the 20/21 academic year and due to the expected (and other possible) restrictions related to COVID-19, activities A2 and A3 will be replaced by a final test (EX) which will have a weight of 50%. The final grade will be calculated A1 · 0.5 + EX · 0.5 if the grade of EX is greater than or equal to 5. Otherwise the final grade of the subject will be EX. The possibility of a resit exam will be maintained for those students with a final grade lower than 5. The grade of the resit exam will replace the EX grade and the maximum final grade in case of having to take this resit exam 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 Public.