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\n  \n 2024\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n On the Importance of Contextualizing an Educational Escape Room Activity.\n \n \n \n \n\n\n \n Gonzalez-Calero, P., A.; Camps-Ortueta, I.; Gutiérrez-Sánchez, P.; and Gómez-Martín, P., P.\n\n\n \n\n\n\n Electronic Journal of e-Learning, 22(4): 43-56. 8 2024.\n \n\n\n\n
\n\n\n\n \n \n \"OnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@article{\n title = {On the Importance of Contextualizing an Educational Escape Room Activity},\n type = {article},\n year = {2024},\n pages = {43-56},\n volume = {22},\n month = {8},\n publisher = {Academic Conferences International Ltd},\n day = {28},\n id = {dbf396ca-ed61-39b8-8703-22c218292070},\n created = {2025-05-19T19:26:48.655Z},\n file_attached = {true},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {8d2b17fe-88c2-3a9d-8e3e-d28b5b0a4c90},\n last_modified = {2025-05-19T19:26:49.100Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper describes the design and evaluation of "Enigma Bio", an educational escape room activity that aims to convey the abstract concept of biodiversity to children between 11 and 13 years of age, making them aware of the importance of climate change and its impact on biodiversity. The design of Enigma Bio is closely linked to the Biodiversity exhibition at the National Museum of Natural Sciences in Madrid, designed for a visit in groups of between 20 and 30 children, with an approximate duration of one hour, running on mobile devices and including augmented reality technology. The purpose of this research is to determine whether, in the case of educational escape room activities in museums with a limited time duration, it is more effective to have a pre-session introducing the topic. Our hypothesis is that without the context of the pre-explanation, the playful component of the game may be too powerful and may cause children not to pay enough attention to the message that the game intends to communicate, and even more so when dealing with a complex message such as the effect of climate change on biodiversity. To answer this research question, we follow an A/B testing experimental design involving two groups of children, one of which received an introductory talk on biodiversity and climate change before going to the museum and the other did not. The experimental design is completed with a pre-post evaluation of the children's environmental awareness by means of a previously validated questionnaire. The results of the experiment provide valuable insights into the effectiveness of the pre-session introduction in enhancing the learning outcomes of short educational escape room activities. Significant differences were observed between pre- and post-activity tests, indicating a moderate overall increase in awareness scores within both individual groups (A and B) as well as across the combined results. The findings suggest that the pre-session introduction indeed plays a role in enhancing students' awareness of the targeted message. These results represent a breakthrough in the e-learning practice that will be of value to other designers of educational escape rooms with a limited time duration.},\n bibtype = {article},\n author = {Gonzalez-Calero, Pedro Antonio and Camps-Ortueta, Irene and Gutiérrez-Sánchez, Pablo and Gómez-Martín, Pedro Pablo},\n doi = {10.34190/ejel.22.4.3199},\n journal = {Electronic Journal of e-Learning},\n number = {4}\n}
\n
\n\n\n
\n This paper describes the design and evaluation of \"Enigma Bio\", an educational escape room activity that aims to convey the abstract concept of biodiversity to children between 11 and 13 years of age, making them aware of the importance of climate change and its impact on biodiversity. The design of Enigma Bio is closely linked to the Biodiversity exhibition at the National Museum of Natural Sciences in Madrid, designed for a visit in groups of between 20 and 30 children, with an approximate duration of one hour, running on mobile devices and including augmented reality technology. The purpose of this research is to determine whether, in the case of educational escape room activities in museums with a limited time duration, it is more effective to have a pre-session introducing the topic. Our hypothesis is that without the context of the pre-explanation, the playful component of the game may be too powerful and may cause children not to pay enough attention to the message that the game intends to communicate, and even more so when dealing with a complex message such as the effect of climate change on biodiversity. To answer this research question, we follow an A/B testing experimental design involving two groups of children, one of which received an introductory talk on biodiversity and climate change before going to the museum and the other did not. The experimental design is completed with a pre-post evaluation of the children's environmental awareness by means of a previously validated questionnaire. The results of the experiment provide valuable insights into the effectiveness of the pre-session introduction in enhancing the learning outcomes of short educational escape room activities. Significant differences were observed between pre- and post-activity tests, indicating a moderate overall increase in awareness scores within both individual groups (A and B) as well as across the combined results. The findings suggest that the pre-session introduction indeed plays a role in enhancing students' awareness of the targeted message. These results represent a breakthrough in the e-learning practice that will be of value to other designers of educational escape rooms with a limited time duration.\n
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\n \n\n \n \n \n \n \n \n A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing.\n \n \n \n \n\n\n \n Gutierrez-Sanchez, P.; Gomez-Martin, M., A.; Gonzalez-Calero, P., A.; and Gomez-Martin, P., P.\n\n\n \n\n\n\n IEEE Transactions on Games. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing},\n type = {article},\n year = {2024},\n keywords = {Chatbots,Games,Logic,Reinforcement learning,Task analysis,Testing,Video games,automated game testing,game-playing AI,regression testing,reinforcement learning,temporal logics},\n publisher = {Institute of Electrical and Electronics Engineers Inc.},\n id = {114d3976-cd62-353a-ac58-571c232ad2bf},\n created = {2025-05-19T19:26:51.738Z},\n file_attached = {true},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {8d2b17fe-88c2-3a9d-8e3e-d28b5b0a4c90},\n last_modified = {2025-05-19T19:26:52.116Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {In video games, the validation of design specifications throughout the development process poses a major challenge as the project grows in complexity and scale and purely manual testing becomes very costly. This paper proposes a new approach to design validation regression testing based on a reinforcement learning technique guided by tasks expressed in a formal logic specification language (TLTL) and the progress made in completing these tasks. This requires no prior knowledge of machine learning to train testing bots, is naturally interpretable and debuggable, and produces dense reward functions without the need for reward shaping. We investigate the validity of our strategy by comparing it to an imitation baseline in experiments organized around three use cases of typical scenarios in commercial video games on a 3D stealth testing environment created in Unity. For each scenario, we analyze the agents&#x0027; reactivity to modifications in common assets to accommodate design needs in other sections&#x00A0;of the game, and their ability to report unexpected gameplay variations. Our experiments demonstrate the practicality of our approach for training bots to conduct automated regression testing in complex video game settings.},\n bibtype = {article},\n author = {Gutierrez-Sanchez, Pablo and Gomez-Martin, Marco A. and Gonzalez-Calero, Pedro A. and Gomez-Martin, Pedro P.},\n doi = {10.1109/TG.2024.3426601},\n journal = {IEEE Transactions on Games}\n}
\n
\n\n\n
\n In video games, the validation of design specifications throughout the development process poses a major challenge as the project grows in complexity and scale and purely manual testing becomes very costly. This paper proposes a new approach to design validation regression testing based on a reinforcement learning technique guided by tasks expressed in a formal logic specification language (TLTL) and the progress made in completing these tasks. This requires no prior knowledge of machine learning to train testing bots, is naturally interpretable and debuggable, and produces dense reward functions without the need for reward shaping. We investigate the validity of our strategy by comparing it to an imitation baseline in experiments organized around three use cases of typical scenarios in commercial video games on a 3D stealth testing environment created in Unity. For each scenario, we analyze the agents' reactivity to modifications in common assets to accommodate design needs in other sections of the game, and their ability to report unexpected gameplay variations. Our experiments demonstrate the practicality of our approach for training bots to conduct automated regression testing in complex video game settings.\n
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\n  \n 2023\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n Reinforcement Learning with Temporal Logic Specifications for Regression Testing NPCs in Video Games.\n \n \n \n\n\n \n Gutierrez-Sanchez, P.; Gomez-Martin, M., A.; Gonzalez-Calero, P., A.; and Gomez-Martin, P., P.\n\n\n \n\n\n\n In IEEE Conference on Computatonal Intelligence and Games, CIG, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{\n title = {Reinforcement Learning with Temporal Logic Specifications for Regression Testing NPCs in Video Games},\n type = {inproceedings},\n year = {2023},\n id = {edc8ea34-fd53-3169-b3a0-c0593e7d9138},\n created = {2024-04-30T05:15:13.057Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {8d2b17fe-88c2-3a9d-8e3e-d28b5b0a4c90},\n last_modified = {2024-04-30T05:15:13.057Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Reinforcement learning (RL) is a promising strategy for the development of autonomous agents in various control and optimization contexts, including the generation of autonomous players in video games. However, designing these agents, and in particular their reward functions to perform sequential decision-making, can be challenging for most users and often require tedious trial-and-error processes until a satisfactory result is obtained. Consequently, these strategies are generally beyond reach for designers and quality control teams, who could potentially make use of them to generate automatic testing agents. This paper presents the application of reinforcement learning and behavioral descriptions given through a formal temporal logic task specification language (TLTL) for the design of NPCs that can be employed as surrogates for the player in such contexts. We argue that these techniques enable designers to naturally specify the way in which they would expect the final player to interact with a level and then generate a test that automatically verifies whether this strategy continues to be feasible throughout the development of the game. We include a series of experiments conducted on a custom 3D test environment developed in Unity3D that show that the proposed methodology provides a simple mechanism for training NPCs in settings that are commonly encountered in modern video games.},\n bibtype = {inproceedings},\n author = {Gutierrez-Sanchez, Pablo and Gomez-Martin, Marco A. and Gonzalez-Calero, Pedro A. and Gomez-Martin, Pedro P.},\n doi = {10.1109/CoG57401.2023.10333208},\n booktitle = {IEEE Conference on Computatonal Intelligence and Games, CIG}\n}
\n
\n\n\n
\n Reinforcement learning (RL) is a promising strategy for the development of autonomous agents in various control and optimization contexts, including the generation of autonomous players in video games. However, designing these agents, and in particular their reward functions to perform sequential decision-making, can be challenging for most users and often require tedious trial-and-error processes until a satisfactory result is obtained. Consequently, these strategies are generally beyond reach for designers and quality control teams, who could potentially make use of them to generate automatic testing agents. This paper presents the application of reinforcement learning and behavioral descriptions given through a formal temporal logic task specification language (TLTL) for the design of NPCs that can be employed as surrogates for the player in such contexts. We argue that these techniques enable designers to naturally specify the way in which they would expect the final player to interact with a level and then generate a test that automatically verifies whether this strategy continues to be feasible throughout the development of the game. We include a series of experiments conducted on a custom 3D test environment developed in Unity3D that show that the proposed methodology provides a simple mechanism for training NPCs in settings that are commonly encountered in modern video games.\n
\n\n\n
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\n \n\n \n \n \n \n \n Diseño iterativo y colaborativo de Educational Escape Rooms en el Museo Nacional de Ciencias Naturales.\n \n \n \n\n\n \n Gutiérrez-Sánchez, P.; Camps-Ortueta, I.; Gómez-Martín, P., P.; and González-Calero, P., A.\n\n\n \n\n\n\n In CEUR Workshop Proceedings, volume 3599, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{\n title = {Diseño iterativo y colaborativo de Educational Escape Rooms en el Museo Nacional de Ciencias Naturales},\n type = {inproceedings},\n year = {2023},\n volume = {3599},\n id = {32271a4e-e2e7-3347-97f7-501cad72dfb7},\n created = {2024-04-30T05:15:13.318Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {8d2b17fe-88c2-3a9d-8e3e-d28b5b0a4c90},\n last_modified = {2024-04-30T05:15:13.318Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This article describes the 3 iterations that took place during the development of an Educational Escape Room at the National Museum of Natural Sciences that aimed to draw students’ attention to the impact of our consumption habits on the loss of Biodiversity. The game designers collaborated with primary and secondary school teachers and museum educators to find good design solutions to bring students closer to the acquisition of concepts such as Biodiversity and connect them to their daily lives, understanding that their actions have an impact.},\n bibtype = {inproceedings},\n author = {Gutiérrez-Sánchez, Pablo and Camps-Ortueta, Irene and Gómez-Martín, Pedro P. and González-Calero, Pedro A.},\n booktitle = {CEUR Workshop Proceedings}\n}
\n
\n\n\n
\n This article describes the 3 iterations that took place during the development of an Educational Escape Room at the National Museum of Natural Sciences that aimed to draw students’ attention to the impact of our consumption habits on the loss of Biodiversity. The game designers collaborated with primary and secondary school teachers and museum educators to find good design solutions to bring students closer to the acquisition of concepts such as Biodiversity and connect them to their daily lives, understanding that their actions have an impact.\n
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\n \n\n \n \n \n \n \n AI Behavior Graphs: A Visual Toolkit for Defining NPC Specifications for Regression Testing.\n \n \n \n\n\n \n Gutiérrez-Sánchez, P.; Gómez-Martín, M., A.; González-Calero, P., A.; and Gómez-Martín, P., P.\n\n\n \n\n\n\n In CEUR Workshop Proceedings, volume 3599, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{\n title = {AI Behavior Graphs: A Visual Toolkit for Defining NPC Specifications for Regression Testing},\n type = {inproceedings},\n year = {2023},\n volume = {3599},\n id = {29e49577-e292-3290-bd12-adf15b2a8b58},\n created = {2024-04-30T05:15:13.600Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {8d2b17fe-88c2-3a9d-8e3e-d28b5b0a4c90},\n last_modified = {2024-04-30T05:15:13.600Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Reinforcement learning (RL) offers a promising approach for developing autonomous agents in various domains, including the creation of in-game characters. However, crafting these agents, and particularly designing reward functions for sequential decision-making, remains a significant challenge, often involving iterative trial-and-error processes until achieving satisfactory results. Consequently, these strategies often elude game designers and quality control teams, who could otherwise use them to automate testing procedures. This paper extends our prior work by introducing “AI Behavior Graphs,” a visual toolkit designed to simplify the creation of behavior specifications for NPCs (Non-Player Characters). Our approach provides an intuitive graphical interface that enables designers to express their expectations for player-NPC interactions within a game level. These specifications are automatically translated into both Linear Temporal Logic (LTL) and Rabin automata, which can in turn be leveraged to dynamically generate reward functions during agent training. This not only expedites NPC development but also makes RL-based methodologies more accessible to a broader audience of game designers and quality assurance teams. Furthermore, it underscores a critical aspect of our approach: the ability to utilize these agents for playtesting game levels. This application ensures continuous validation of designer expectations throughout the development cycle, enhancing the overall game design process.},\n bibtype = {inproceedings},\n author = {Gutiérrez-Sánchez, Pablo and Gómez-Martín, Marco A. and González-Calero, Pedro A. and Gómez-Martín, Pedro P.},\n booktitle = {CEUR Workshop Proceedings}\n}
\n
\n\n\n
\n Reinforcement learning (RL) offers a promising approach for developing autonomous agents in various domains, including the creation of in-game characters. However, crafting these agents, and particularly designing reward functions for sequential decision-making, remains a significant challenge, often involving iterative trial-and-error processes until achieving satisfactory results. Consequently, these strategies often elude game designers and quality control teams, who could otherwise use them to automate testing procedures. This paper extends our prior work by introducing “AI Behavior Graphs,” a visual toolkit designed to simplify the creation of behavior specifications for NPCs (Non-Player Characters). Our approach provides an intuitive graphical interface that enables designers to express their expectations for player-NPC interactions within a game level. These specifications are automatically translated into both Linear Temporal Logic (LTL) and Rabin automata, which can in turn be leveraged to dynamically generate reward functions during agent training. This not only expedites NPC development but also makes RL-based methodologies more accessible to a broader audience of game designers and quality assurance teams. Furthermore, it underscores a critical aspect of our approach: the ability to utilize these agents for playtesting game levels. This application ensures continuous validation of designer expectations throughout the development cycle, enhancing the overall game design process.\n
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Liquid Snake: a test environment for video game testing agents.\n \n \n \n\n\n \n Gutiérrez-Sánchez, P.; Gómez-Martín, M., A.; González-Calero, P., A.; and Gómez-Martín, P., P.\n\n\n \n\n\n\n In CEUR Workshop Proceedings, volume 3305, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n
@inproceedings{\n title = {Liquid Snake: a test environment for video game testing agents},\n type = {inproceedings},\n year = {2022},\n volume = {3305},\n id = {0b3fd9d1-6933-39b5-8146-2f0f2c4eab4f},\n created = {2024-04-30T05:15:13.858Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {8d2b17fe-88c2-3a9d-8e3e-d28b5b0a4c90},\n last_modified = {2024-04-30T05:15:13.858Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {In recent years, a number of benchmarks and test environments have been proposed for research on AI algorithms that have made it possible to evaluate and accelerate development in this field. There exists, however, an absence of environments in which to evaluate the feasibility of such algorithms in the context of games intended for continuous development, in particular in regression testing and automatic error detection tasks in commercial video games. In this paper we propose a new test-bed - Liquid Snake: a 3D third-person stealth game prototype, designed to conveniently integrate autonomous agent-driven quality control mechanisms into the development life cycle of a video game, based on the open source ML-Agents library in Unity3D. Focusing on the problem of regression testing on the potential unexpected changes induced in a game by altering the AI of enemies, we argue that this environment lends itself to be used as a sample test environment for automated QA methodologies thanks to the complexity and variety in the behaviors of NPCs naturally present in stealth titles.},\n bibtype = {inproceedings},\n author = {Gutiérrez-Sánchez, Pablo and Gómez-Martín, Marco A. and González-Calero, Pedro A. and Gómez-Martín, Pedro P.},\n booktitle = {CEUR Workshop Proceedings}\n}
\n
\n\n\n
\n In recent years, a number of benchmarks and test environments have been proposed for research on AI algorithms that have made it possible to evaluate and accelerate development in this field. There exists, however, an absence of environments in which to evaluate the feasibility of such algorithms in the context of games intended for continuous development, in particular in regression testing and automatic error detection tasks in commercial video games. In this paper we propose a new test-bed - Liquid Snake: a 3D third-person stealth game prototype, designed to conveniently integrate autonomous agent-driven quality control mechanisms into the development life cycle of a video game, based on the open source ML-Agents library in Unity3D. Focusing on the problem of regression testing on the potential unexpected changes induced in a game by altering the AI of enemies, we argue that this environment lends itself to be used as a sample test environment for automated QA methodologies thanks to the complexity and variety in the behaviors of NPCs naturally present in stealth titles.\n
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