Relevant bibliographies by topics / AI-computer

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI-computer'

Author: Grafiati
Published: 24 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "AI-computer"

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Nareyek, Alexander. "AI in Computer Games." Queue 1, no. 10 (February 2004): 58–65. http://dx.doi.org/10.1145/971564.971593.

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Jaimes, Alex. "Computer Vision Startups Tackle AI." IEEE MultiMedia 23, no. 4 (October 2016): 94–96. http://dx.doi.org/10.1109/mmul.2016.62.

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Camstra, Bert. "AI in computer-based training." British Journal of Educational Technology 39, no. 2 (March 2008): 297–303. http://dx.doi.org/10.1111/j.1467-8535.2008.00820.x.

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Cass, S. "Mind games [computer game AI]." IEEE Spectrum 39, no. 12 (December 2002): 40–44. http://dx.doi.org/10.1109/mspec.2002.1088444.

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Kersting, Kristian. "Rethinking Computer Science Through AI." KI - Künstliche Intelligenz 34, no. 4 (December 2020): 435–37. http://dx.doi.org/10.1007/s13218-020-00692-5.

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Bouzy, Bruno, and Tristan Cazenave. "Computer Go: An AI oriented survey." Artificial Intelligence 132, no. 1 (October 2001): 39–103. http://dx.doi.org/10.1016/s0004-3702(01)00127-8.

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Chauvette, Peter. "Formalism in AI and computer science." Information Processing & Management 28, no. 2 (January 1992): 284–85. http://dx.doi.org/10.1016/0306-4573(92)90055-5.

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Malcolm, Grant. "Formalism in AI and computer science." Science of Computer Programming 17, no. 1-3 (December 1991): 267–69. http://dx.doi.org/10.1016/0167-6423(91)90052-y.

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Frey, Peter W. "Memory Based Expertise: Computer Chess vs. AI." ICGA Journal 14, no. 4 (December 1, 1991): 192–97. http://dx.doi.org/10.3233/icg-1991-14403.

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Khoo, A., and R. Zubek. "Applying inexpensive AI techniques to computer games." IEEE Intelligent Systems 17, no. 4 (July 2002): 48–53. http://dx.doi.org/10.1109/mis.2002.1024752.

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Dissertations / Theses on the topic "AI-computer"

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Mehta, Manish. "Construction and adaptation of AI behaviors in computer games." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42724.

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Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC's in computer games is that construction of high quality AI behaviors is a hard problem. There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process restricts it to certain individuals who have a certain expertise in this area. There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments so that novice users (without programming and design experience) can carry out the behavior authoring task. Second, the constructed AI behaviors have problems and bugs in them which cause a break in player expe- rience when the problematic behaviors repeatedly fail. It is harder for novice users to identify, modify and correct problems with the authored behavior sets as they do not have the necessary debugging and design experience. The two issues give rise to a couple of interesting questions that need to be investigated: a) How can the AI behavior construction process be simplified so that a novice user (without program- ming and design experience) can easily conduct the authoring activity and b) How can the novice users be supported to help them identify and correct problems with the authored behavior sets? In this thesis, I explore the issues related to the problems highlighted and propose a solution to them within an application domain, named Second Mind(SM). In SM novice users who do not have expertise in computer programming employ an authoring interface to design behaviors for intelligent virtual characters performing a service in a virtual world. These services range from shopkeepers to museum hosts. The constructed behaviors are further repaired using an AI based approach. To evaluate the construction and repair approach, we conduct experiments with human subjects. Based on developing and evaluating the solution, I claim that a design solution with behavior timeline based interaction design approach for behavior construction supported by an understandable vocabulary and reduced feature representation for- malism enables novice users to author AI behaviors in an easy and understandable manner for NPCs performing a service in a virtual world. I further claim that an introspective reasoning approach based on comparison of successful and unsuccessful execution traces can be used as a means to successfully identify breaks in player ex- perience and modify the failures to improve the experience of the player interacting with NPCs performing a service in a virtual world. The work contributes in the following three ways by providing: 1) a novel introspective reasoning approach for successfully detecting and repairing failures in AI behaviors for NPCs performing a service in a virtual world.; 2) a novice user understandable authoring environment to help them create AI behaviors for NPCs performing a service in a virtual world in an easy and understandable manner; and 3) Design, debugging and testing scaffolding to help novice users modify their authored AI behaviors and achieve higher quality modified AI behaviors compared to their original unmodified behaviors.
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Zagoraiou, Maroussa <1979&gt. "Esperimenti per modelli parzialmente lineari con applicazione ai computer experiments." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/668/.

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Bellinder, Tobias. "Scrabble-AI : En Scrabble-AI med inriktning på ”Fun-AI”." Thesis, University of Skövde, School of Humanities and Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4055.

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Scrabble är ett populärt korsordsliknande brädspel som funnits på svenska sedan 1950-talet. Datorprogram som spelar Scrabble har funnits sedan och 1980-talet. De flesta har haft som fokus att spela så optimalt som möjligt och utmana de bästa mänskliga spelarna. Det här arbetet undersöker hur man kan skapa en ”Fun-AI” för Scrabble som fokuserar på spelarens upplevelse och som lämpar sig för alla spelare oavsett nivå. Tre enkla AI-spelare skapas genom att använda grundläggande egenskaper som karakteriserar mänskligt spel. Utvärderingen visar att en relativt outvecklad AI tas för en människa av försökspersoner som spelat matcher mot AI-spelaren. Arbetet kan användas som grund för att gå vidare och utveckla AI:n genom att modellera fler och mer avancerade beteenden hos mänskliga spelare.

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Dahlbom, Anders. "An adaptive AI for real-time strategy games." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-908.

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In real-time strategy (RTS) games, the human player faces tasks such as resource allocation, mission planning, and unit coordination. An Artificial Intelligence (AI) system that acts as an opponent against the human player need to be quite powerful, in order to create one cohesive strategy for victory. Even though the goal for an AI system in a computer game is not to defeat the human player, it might still need to act intelligently and look credible. It might however also need to provide just enough difficulty, so that both novice and expert players appreciates the game. The behavior of computer controlled opponents in RTS games of today has to a large extent been based on static algorithms and structures. Furthermore, the AI in RTS games performs the worst at the strategic level, and many of the problems can be tracked to its static nature. By introducing an adaptive AI at the strategic level, many of the problems could possibly be solved, the illusion of intelligence might be strengthened, and the entertainment value could perhaps be increased.

The aim of this dissertation has been to investigate how dynamic scripting, a technique for achieving adaptation in computer games, possibly could be applied at the strategic level in an RTS game. The dynamic scripting technique proposed by Spronck, et al. (2003), was originally intended for computer role-playing games (CRPGs), where it was used for online creation of scripts to control non-player characters (NPCs). The focus in this dissertation has been to investigate: (1) how the structure of dynamic scripting possibly could be modified to fit the strategic level in an RTS game, (2) how the adaptation time possibly could be lowered, and (3) how the performance of dynamic scripting possibly could be throttled.

A new structure for applying dynamic scripting has been proposed: a goal-rule hierarchy, where goals are used as domain knowledge for selecting rules. A rule is seen as a strategy for achieving a goal, and a goal can in turn be realized by several different rules. The adaptation process operates on the probability of selecting a specific rule as strategy for a specific goal. Rules can be realized by sub-goals, which create a hierarchical system. Further, a rule can be coupled with preconditions, which if false initiates goals with the purpose of fulfilling them. This introduces planning.

Results have shown that it can be more effective, with regard to adaptation time, re-adaptation time, and performance, to have equal punishment and reward factors, or to have higher punishments than rewards, compared to having higher rewards than punishments. It has also been shown that by increasing the learning rate, or including the derivative, both adaptation, and re-adaptation times, can effectively be lowered.

Finally, this dissertation has shown that by applying a fitness-mapping function, the performance of the AI can effectively be throttled. Results have shown that learning rate, and maximum weight setting, also can be used to vary the performance, but not to negative performance levels.

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Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.

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In the last decade, Machine Learning techniques have been used in different fields, ranging from finance to healthcare and even marketing. Amongst all these techniques, the ones adopting a Deep Learning approach were revealed to outperform humans in tasks such as object detection, image classification and speech recognition. This thesis introduces the concept of Edge AI: that is the possibility to build learning models capable of making inference locally, without any dependence on expensive servers or cloud services. A first case study we consider is based on the Google AIY Vision Kit, an intelligent camera equipped with a graphic board to optimize Computer Vision algorithms. Then, we test the performances of CORe50, a dataset for continuous object recognition, on embedded systems. The techniques developed in these chapters will be finally used to solve a challenge within the Audi Autonomous Driving Cup 2018, where a mobile car equipped with a camera, sensors and a graphic board must recognize pedestrians and stop before hitting them.
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Ai, Renlong [Verfasser], and Hans [Akademischer Betreuer] Uszkoreit. "Speech verification for computer assisted pronunciation training / Renlong Ai ; Betreuer: Hans Uszkoreit." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2018. http://d-nb.info/1169132510/34.

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Ai, Renlong Verfasser], and Hans [Akademischer Betreuer] [Uszkoreit. "Speech verification for computer assisted pronunciation training / Renlong Ai ; Betreuer: Hans Uszkoreit." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2018. http://d-nb.info/1169132510/34.

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Axelsson, Mattis, and Sara Larsson. "Utvecklande AI : En studie i hur man skapar ett system för lärande AI." Thesis, Blekinge Tekniska Högskola, Sektionen för planering och mediedesign, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4587.

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AI är något som blir allt viktigare inom dagens spel och får allt högre krav på att agera mänskligt och intelligent. Detta kandidatarbete undersöker vilken metod som är att föredra för att skapa en AI som kan lära sig av sina tidigare erfarenheter. Några av de metoder som undersöks är trädstrukturer, Artificial Neural Network och GoCap. Genom att skapa en applikation med en av metoderna samt göra en undersökning på hur AI:n i applikationen upplevdes fick vi resultat om denna metod var användbar. Utifrån detta diskuteras det ifall andra metoder hade varit mer effektiva, hur man hade kunnat förbättra AI:n samt hur framtiden för spel-AI skulle kunna se ut.
AI is something that has become more important in today’s games and gets higher pressure to act human and intelligent. This thesis examines which methods are preferred when creating an AI that can learn from its previous experiences. Some of the methods that are examined are tree structures, Artificial Neural Network and GoCap. By creating an application with one of the methods and a survey of how the AI in the application was perceived we got a result that showed us if the method was functional. From this we discuss if the other methods would have been more effective, how we could have improved the AI and what the future for game-AI holds.
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Yannakakis, Georgios N. "AI in computer games : generating interesting interactive opponents by the use of evolutionary computation." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/879.

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Which features of a computer game contribute to the player’s enjoyment of it? How can we automatically generate interesting and satisfying playing experiences for a given game? These are the two key questions addressed in this dissertation. Player satisfaction in computer games depends on a variety of factors; here the focus is on the contribution of the behaviour and strategy of game opponents in predator/prey games. A quantitative metric of the ‘interestingness’ of opponent behaviours is defined based on qualitative considerations of what is enjoyable in such games, and a mathematical formulation grounded in observable data is derived. Using this metric, neural-network opponent controllers are evolved for dynamic game environments where limited inter-agent communication is used to drive spatial coordination of opponent teams. Given the complexity of the predator task, cooperative team behaviours are investigated. Initial candidates are generated using off-line learning procedures operating on minimal neural controllers with the aim of maximising opponent performance. These example controllers are then adapted using on-line (i.e. during play) learning techniques to yield opponents that provide games of high interest. The on-line learning methodology is evaluated using two dissimilar predator/prey games with a number of different computer player strategies. It exhibits generality across the two game test-beds and robustness to changes of player, initial opponent controller selected, and complexity of the game field. The interest metric is also evaluated by comparison with human judgement of game satisfaction in an experimental survey. A statistically significant number of players were asked to rank game experiences with a test-bed game using perceived interestingness and their ranking was compared with that of the proposed interest metric. The results show that the interest metric is consistent with human judgement of game satisfaction. Finally, the generality, limitations and potential of the proposed methodology and techniques are discussed, and other factors affecting the player’s satisfaction, such as the player’s own strategy, are briefly considered. Future directions building on the work described herein are presented and discussed.
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Lima, Anderson Silva, and Andreas Blixt. "Investigating the possibility of bias against AI-computercomposed music." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19963.

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This study explores how respondents perceive human-composed music and AI-computer-composed music. The aim was to find out if there is a negative bias against AI-computer-composed music. The research questions are 1. How is AI-computer-composed music perceived compared to human-composed music? 2. Are there prejudices towards AI-computer-composed music? If yes, what are the prejudices? Four participants took part in a qualitative experiment and a semi-structured interview. Two music pieces were used as artifacts, one was human-composed, and the AI-computer AIVA composed the other. The results showed that although the researchers have not revealed to the participants if they had chosen the AI-computer-composed song or the human-composed song as their favorite, all the participants strongly believed that their favorite song was human-composed. Thus, indicating a bias towards human-composed music The results also showed that the two music pieces were not perceived to have the same characteristics or evoke the same emotions; furthermore, there was some skepticism, whether an AI-computer-composed song could recall the same emotions as a human-composed song. However, none of the respondents explicitly expressed negativity towards AI-computer-composed music.
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Books on the topic "AI-computer"

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Leith, Philip. Formalism in AI and computer science. New York: E. Horwood, 1990.

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Meyer, John-Jules Ch. Epistemic logic for AI and computer science. Cambridge: Cambridge University Press, 1995.

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Ruqian, Lu. Automatic generation of computer animation: Using AI for movie animation. Berlin: Springer, 2002.

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Scalisi, Raffaella. Users: Storia dell'interazione uomo-macchina dai mainframe ai computer indossabili. Milano: Guerini e associati, 2001.

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Ruqian, Lu. Automatic generation of computer animation: Using AI for movie animation. Berlin: Springer, 2002.

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Songmao, Zhang, ed. Automatic generation of computer animation: Using AI for movie animation. Berlin: Springer, 2002.

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Knowledge and computing: A course on computer epistemology. Budapest: Central European University Press, 2010.

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Blondie24: Playing at the edge of AI. San Francisco, Calif: Morgan Kaufmann, 2002.

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Rineke, Verbrugge, ed. Teamwork in multi-agent systems: A formal approach. Hoboken, NJ: John Wiley, 2010.

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Bramer, M. A. (Max A.), 1948-, ed. Artificial intelligence in theory and practice: IFIP 19th World Computer Congress, TC 12: IFIP AI 2006 Stream, August 21-24, 2006, Santiago, Chile. New York: Springer, 2011.

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Book chapters on the topic "AI-computer"

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Fisher, Robert B. "Is computer vision still AI?" In Advances in Artificial Intelligence, 239. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61859-7_32.

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Cai, Xindi, and Donald C. Wunsch. "Computer Go: A Grand Challenge to AI." In Challenges for Computational Intelligence, 443–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71984-7_16.

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Birnbaum, Lawrence, William Ferguson, and Menachem Jona. "Using Content-based AI to Structure Human-Computer Interaction." In Communication from an Artificial Intelligence Perspective, 191–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-58146-5_10.

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Ho, Carlos S. N., and Richard L. Grimsdale. "A Behavioural control framework for computer animation using AI techniques." In Lecture Notes in Computer Science, 381–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60697-1_125.

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Le, Nguyen-Thinh, Kristy Elizabeth Boyer, Beenish Chaudry, Barbara Di Eugenio, Sharon I.-Han Hsiao, and Leigh Ann Sudol-DeLyser. "The First Workshop on AI-supported Education for Computer Science (AIEDCS)." In Lecture Notes in Computer Science, 947–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39112-5_159.

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Modrzejewski, Mateusz, and Przemysław Rokita. "Implementation of Generic Steering Algorithms for AI Agents in Computer Games." In Studies in Big Data, 15–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77604-0_2.

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Wang, Senzhang, and Jiannong Cao. "AI and Deep Learning for Urban Computing." In Urban Informatics, 815–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_43.

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AbstractIn the big data era, with the large volume of available data collected by various sensors deployed in urban areas and the recent advances in AI techniques, urban computing has become increasingly important to facilitate the improvement of people’s lives, city operation systems, and the environment. In this chapter, we introduce the challenges, methodologies, and applications of AI techniques for urban computing. We first introduce the background, followed by listing key challenges from the perspective of computer science when AI techniques are applied. Then we briefly introduce the AI techniques that are widely used in urban computing, including supervised learning, semi-supervised learning, unsupervised learning, matrix factorization, graphic models, deep learning, and reinforcement learning. With the recent advances of deep-learning techniques, models such as CNN and RNN have shown significant performance gains in many applications. Thus, we briefly introduce the deep-learning models that are widely used in various urban-computing tasks. Finally, we discuss the applications of urban computing including urban planning, urban transportation, location-based social networks (LBSNs), urban safety and security, and urban-environment monitoring. For each application, we summarize major research challenges and review previous work that uses AI techniques to address them.
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Teti, Antonio. "La nuova frontiera dell’hardware: dai microchip magnetici, ottici e chimici ai computer quantici." In Il futuro dell’Information & Communication Technology, 113–20. Milano: Springer Milan, 2009. http://dx.doi.org/10.1007/978-88-470-1388-9_9.

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Roanes-Lozano, Eugenio, Luis M. Laita, and Eugenio Roanes-Macías. "An application of an AI methodology to railway interlocking systems using computer algebra." In Tasks and Methods in Applied Artificial Intelligence, 687–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64574-8_455.

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Kind, Adrian, and George Azzopardi. "An Explainable AI-Based Computer Aided Detection System for Diabetic Retinopathy Using Retinal Fundus Images." In Computer Analysis of Images and Patterns, 457–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29888-3_37.

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Conference papers on the topic "AI-computer"

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Mundy, Joseph L., Yi Dong, A. Gilliam, and R. Wagner. "The semantic web and computer vision: old AI meets new AI." In Automatic Target Recognition XXVIII, edited by Firooz A. Sadjadi and Abhijit Mahalanobis. SPIE, 2018. http://dx.doi.org/10.1117/12.2322746.

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Chiang, Akcell. "Motivate AI Class with Interactive Computer Game." In 2007 IEEE International Workshop on Digital Games and Intelligent Toys-based Education. IEEE, 2007. http://dx.doi.org/10.1109/digitel.2007.34.

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Gallego, Francisco, Abel Bernabeu, Juan Reverte, Rosana Satorre, and Faraon Llorens. "A Computer-Games-Based AI Research Environment." In 2006 Fifth Mexican International Conference on Artificial Intelligence. IEEE, 2006. http://dx.doi.org/10.1109/micai.2006.2.

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Menkovski, Vlado, and Dimitrios Metafas. "AI Model for Computer games based on Case Based Reasoning and AI Planning." In the 3rd international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1413634.1453242.

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Tiller, John A. "The implementation of AI technologies in computer wargames." In Defense and Security, edited by Dawn A. Trevisani and Alex F. Sisti. SPIE, 2004. http://dx.doi.org/10.1117/12.548276.

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"Track 01 AI, Computer Networks, Security & IOT." In 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2019. http://dx.doi.org/10.1109/wiecon-ece48653.2019.9019925.

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Ji, Lixia, and Jianhong Ma. "Behavior tree for complex computer game AI behavior." In International Conference on Simulation and Modeling Methodologies, Technologies and Applications. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/smta140241.

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McAllister, Jeff, and Uri Levy. "Faster, smarter computer vision with AI and OpenCL." In IWOCL 2017: 5th International Workshop on OpenCL. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3078155.3078164.

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Bradáč, Vladimír, and Pavel Smolka. "Paper-based vs. AI computer-based test results." In CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5114044.

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Mittal, Varnit, and Bharat Bhushan. "Accelerated Computer Vision Inference with AI on the Edge." In 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2020. http://dx.doi.org/10.1109/csnt48778.2020.9115770.

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Reports on the topic "AI-computer"

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Rodriguez, Simon, Autumn Toney, and Melissa Flagg. Patent Landscape for Computer Vision: United States and China. Center for Security and Emerging Technology, September 2020. http://dx.doi.org/10.51593/20200054.

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China’s surge in artificial intelligence development has been fueled, in large part, by advances in computer vision, the AI subdomain that makes powerful facial recognition technologies possible. This data brief compares U.S. and Chinese computer vision patent data to illustrate the different approaches each country takes to AI development.
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Murdick, Dewey, Daniel Chou, Ryan Fedasiuk, and Emily Weinstein. The Public AI Research Portfolio of China’s Security Forces. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/20200057.

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New analytic tools are used in this data brief to explore the public artificial intelligence (AI) research portfolio of China’s security forces. The methods contextualize Chinese-language scholarly papers that claim a direct working affiliation with components of the Ministry of Public Security, People's Armed Police Force, and People’s Liberation Army. The authors review potential uses of computer vision, robotics, natural language processing and general AI research.
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