2,387,059 research outputs found

    Biomimetic characteristics of dual TLC retention mechanism

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    Most biomimetic chromatography measurements provide information on the ability of drugs to pass through cell membranes, their interaction with protein-based structures and distribution properties. The biomimetic properties of thin-layer chromatography (TLC) conditions have not been investigated so far. In our previous research, the presence of dual retention mechanisms for selected imidazoline and serotonin receptor ligands was confirmed under TLC conditions on C-18, diol, and a silica-based phases. The mobile phase was amixture of ACN and water with 20 mM ammoniumacetate and 0.1 volume %of acetic acid [1]. In this research, the average retention parameters were determined by using the integration procedure [2]. The parameter RMH is the average retention in the hydrophilicdominated (HILIC), while RMR is the average retention in the region of reversed-phase interactions (RP). The parameter RMA corresponds to the average retention within the overall HILIC/RP region. The lipophilicity successfully correlates with the C-18 and silica based behaviour. For plasma protein binding affinity, the best correlations were found within C-18 and silica-based systems (r > 0.70). There is also a correlation of average silica gel and C-18 mechanism of interaction with the volume of distribution (r > 0.73), and the intestinal absorption (r > 0.70). The retention behaviour on the diol phase showed a good correlation with the P-gp inhibitor activity (r = 0.80). TLC systems that provide dual retention mechanisms can be successfully used in the rapid biomimetic profiling of serotonin and imidazoline receptor ligands in the first steps of drug discovery.10th IAPC Meeting, Book of Abstract

    GIS analysis of cropping systems: proceedings of an International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region,18-19 Aug 1997, ICRISAT-Patancheru, India

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    Geographic information systems (GIS) have come a long way from obscurity in the 1980s to now become commonplace in universities, international research institutions, government departments, and private businesses where the technology is used for a wide range of applications. In the last few years, its application has been increasing in agricultural research and development. The International Workshop on Harmonization of Databases for GI S Analysis of Cropping Systems in the Asia Region, held 18-19 Aug 1997 at ICRISAT, Patancheru, India examined the current status of available software options, database requirements, availability of data, database storage and exchange procedures, options for GI S outputs and optimization of regional interactions in the use of GI S for cropping system analysis wi t h respect to Asia. GI S specialists from international agricultural research centers (IARCs) and national agricultural research systems (NARS) of Asia reviewed state-of-the-art know-how in using GI S as a research tool for the characterization of target environments, soil, water and nutrient management, integrated pest and disease management, and sustainable land-use systems. The workshop focussed on three basic questions: "what information is available?", "in what form is the information available?", and "in what form should the GI S output be?" Recommendations were made on the effective use of GI S and on the possibility of harmonizing datasets for common use by IARCs and NARS. The workshop was followed by a hands-on training program on the use of GI S in analysis of cropping systems of Bangladesh, India, Nepal, Pakistan, and Sri Lanka. The country case studies prepared during this training program wi l l be published as a separate volume. The present publication includes status papers describing GI S as a research tool, types of GI S software available and its use in different institutions

    Interview with Peter Mertens and Wolfgang König: “From Reasonable Automation to (Sustainable) Autonomous Systems”

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    Peter Mertens is Professor Emeritus of Wirtschaftsinformatik at the Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg. After studying industrial engineering, he completed his doctoral studies and his habilitation at the TH Darmstadt (1961) and the TU München (1966), respectively. From 1966 to 1968, he worked for a large software and consulting firm in Switzerland, first as a system designer and later as a managing director. In 1968, Peter Mertens took over the first chaired professorship specialized in business data processing at the University of Linz. He is considered one of the founding fathers of Wirtschaftsinformatik in the German-speaking world. Until September 2005, Peter Mertens held the Chair of Business Administration, especially Wirtschaftsinformatik I at the Faculty of Business and Social Sciences of FAU. In parallel, he was head of the computer science research group “Business Applications” at FAU’s Faculty of Engineering. Since fall 2005, he works as an emeritus professor at his former chair. Peter Mertens is the author of numerous books, including 23 monographs. He has also been involved in the editing of 26 collective works. The first volume of his book “Integrated Information Processing” has been published in 18 editions. Some of his books have been translated into English, Chinese, Italian, and Russian. Among other awards, he is a Fellow of the German Informatics Society, an honorary doctor of five universities in Germany, Austria, and Switzerland, and has been awarded the Order of Merit of the Federal Republic of Germany. From 1990 until 2000, Peter Mertens served as Editor-in-Chief for WIRTSCHAFTSINFORMATIK (now: BISE). Until 2016, Wolfgang König was Professor of Business Administration, especially Information Systems and Information Management at the Faculty of Economics and Business Administration of Goethe University Frankfurt a. M., and until January 2022, he was Chairman of the E-Finance Lab (since 2020: efl – the Data Science Institute) at Goethe University. Since 2008, he holds the position of Executive Director of the House of Finance of Goethe University, and since 2016, he serves as Senior Professor at Goethe University. From 1998 until 2008, König served as Editor-in-Chief for WIRTSCHAFTSINFORMATIK (now: BISE). Both Peter Mertens and Wolfgang König are clearly among the research pioneers when it comes to automated systems, which can be seen as a precursor of the central topic of this special issue: autonomous systems (AS). The key difference between automated systems and AS is that, in AS, machines or other technology actors have at least some agency (i.e., they can act autonomously), whereas in automated systems, the agency still lies with humans – who, for example, define the relevant rule system – and machines/technologies merely automate the execution of these predefined rules

    Partial Correctness of a Power Algorithm

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    This work continues a formal verification of algorithms written in terms of simple-named complex-valued nominative data [6],[8],[15],[11],[12],[13]. In this paper we present a formalization in the Mizar system [3],[1] of the partial correctness of the algorithm: i := val.1 j := val.2 b := val.3 n := val.4 s := val.5 while (i n) i := i + j s := s * b return s computing the natural n power of given complex number b, where variables i, b, n, s are located as values of a V-valued Function, loc, as: loc/.1 = i, loc/.3 = b, loc/.4 = n and loc/.5 = s, and the constant 1 is located in the location loc/.2 = j (set V represents simple names of considered nominative data [17]).The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2],[4] with partial pre- and post-conditions [14],[16],[7],[5].Institute of Informatics, University of Białystok, PolandGrzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, and Karol Pąk. The role of the Mizar Mathematical Library for interactive proof development in Mizar. Journal of Automated Reasoning, 61(1):9–32, 2018. doi:10.1007/s10817-017-9440-6.R.W. Floyd. Assigning meanings to programs. Mathematical aspects of computer science, 19(19–32), 1967.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.C.A.R. Hoare. An axiomatic basis for computer programming. Commun. ACM, 12(10): 576–580, 1969.Ievgen Ivanov and Mykola Nikitchenko. On the sequence rule for the Floyd-Hoare logic with partial pre- and post-conditions. In Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kyiv, Ukraine, May 14–17, 2018, volume 2104 of CEUR Workshop Proceedings, pages 716–724, 2018.Ievgen Ivanov, Mykola Nikitchenko, Andrii Kryvolap, and Artur Korniłowicz. Simple-named complex-valued nominative data – definition and basic operations. Formalized Mathematics, 25(3):205–216, 2017. doi:10.1515/forma-2017-0020.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. Implementation of the composition-nominative approach to program formalization in Mizar. The Computer Science Journal of Moldova, 26(1):59–76, 2018.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. On an algorithmic algebra over simple-named complex-valued nominative data. Formalized Mathematics, 26(2):149–158, 2018. doi:10.2478/forma-2018-0012.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. An inference system of an extension of Floyd-Hoare logic for partial predicates. Formalized Mathematics, 26(2): 159–164, 2018. doi:10.2478/forma-2018-0013.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. Partial correctness of GCD algorithm. Formalized Mathematics, 26(2):165–173, 2018. doi:10.2478/forma-2018-0014.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. On algebras of algorithms and specifications over uninterpreted data. Formalized Mathematics, 26(2):141–147, 2018. doi:10.2478/forma-2018-0011.Artur Kornilowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. Formalization of the algebra of nominative data in Mizar. In Maria Ganzha, Leszek A. Maciaszek, and Marcin Paprzycki, editors, Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, September 3–6, 2017., pages 237–244, 2017. ISBN 978-83-946253-7-5. doi:10.15439/2017F301.Artur Kornilowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. Formalization of the nominative algorithmic algebra in Mizar. In Leszek Borzemski, Jerzy Świątek, and Zofia Wilimowska, editors, Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017 – Part II, Szklarska Poręba, Poland, September 17–19, 2017, volume 656 of Advances in Intelligent Systems and Computing, pages 176–186. Springer, 2017. ISBN 978-3-319-67228-1. doi:10.1007/978-3-319-67229-8_16.Artur Korniłowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. An approach to formalization of an extension of Floyd-Hoare logic. In Vadim Ermolayev, Nick Bassiliades, Hans-Georg Fill, Vitaliy Yakovyna, Heinrich C. Mayr, Vyacheslav Kharchenko, Vladimir Peschanenko, Mariya Shyshkina, Mykola Nikitchenko, and Aleksander Spivakovsky, editors, Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, May 15–18, 2017, volume 1844 of CEUR Workshop Proceedings, pages 504–523. CEUR-WS.org, 2017.Artur Korniłowicz, Ievgen Ivanov, and Mykola Nikitchenko. Kleene algebra of partial predicates. Formalized Mathematics, 26(1):11–20, 2018. doi:10.2478/forma-2018-0002.Andrii Kryvolap, Mykola Nikitchenko, and Wolfgang Schreiner. Extending Floyd-Hoare logic for partial pre- and postconditions. In Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, and Grygoriy Zholtkevych, editors, Information and Communication Technologies in Education, Research, and Industrial Applications: 9th International Conference, ICTERI 2013, Kherson, Ukraine, June 19–22, 2013, Revised Selected Papers, pages 355–378. Springer International Publishing, 2013. ISBN 978-3-319-03998-5. doi:10.1007/978-3-319-03998-5_18.Volodymyr G. Skobelev, Mykola Nikitchenko, and Ievgen Ivanov. On algebraic properties of nominative data and functions. In Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, and Grygoriy Zholtkevych, editors, Information and Communication Technologies in Education, Research, and Industrial Applications – 10th International Conference, ICTERI 2014, Kherson, Ukraine, June 9–12, 2014, Revised Selected Papers, volume 469 of Communications in Computer and Information Science, pages 117–138. Springer, 2014. ISBN 978-3-319-13205-1. doi:10.1007/978-3-319-13206-8_6.27218919

    Impacts of optimized cold & hot deck reset schedules on dual duct VAV system – Application and results

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    Abstract In the theory and modeling part, the principles of VAV box optimization are investigated. The simulations show that optimizing cold and hot deck reset schedules can significantly reduce the amount of energy consumption. During a Continuous Commissioning (CC) process, the optimal cold and hot deck reset schedules were implemented in 12 dual duct variable air volume systems which serve a 324,000 ft2 university buildmg. The improved cold and hot deck reset schedules combined with other CC measures reduced the chilled water energy consumption by 7,571 MMEtu/yr (18%) and 303 MWh/yr (18%) for the fan power. The estimated heating energy savings are 2,327 MMBtu/yr. The estimated annual cost savings are $75,04O/yr. This paper presents the building and AHU system information, optimal reset schedule, and measured results. Introduction Zachry Engineering Center is a major engineering research and teaching building at the Texas A&M Campus. The building was built i

    Адаптивний підхід до управління інформаційною безпекою

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    Обсяг роботи 90 сторінок, 29 таблиць, 3 рисунки та 18 джерел за переліком посилань. Актуальність роботи обумовлюється необхідністю створення нових підходів та методів управління інформаційною безпекою для побудови надійних систем захисту інформації. Мета роботи полягає у формулюванні основних положень адаптивного підходу до управління інформаційною безпекою та визначенні прийнятного (ефективного) рівня інвестицій в побудову СЗІ. Об’єктом дослідження є можливі підходи до управління інформаційною безпекою. Предметом дослідження є адаптивний підхід до управління інформаційною безпекою. Наукова новизна одержаних результатів обумовлюється запропонованим способом обчислення прийнятного (ефективного) обсягу інвестицій у систему захисту інформації, структура і функції якої формуються виходячи із принципу адаптивного управління ІБ організації. Практичне значення одержаних результатів полягає в можливості застосування запропонованого способу для обчислення ефективного обсягу інвестицій у систему захисту інформації організацій.This work contains 90 pages, 29 tables, 3 figures and 18 references are cited. Urgency of the work is due to the need to create new approaches and methods of information security management to build reliable information security systems. The goal of the work is to formulate the main aspects of an adaptive approach to information security management and to determine the acceptable (effective) level of investment in building a KIC. The object of the research is an adaptive approach to information security management. The subject of the research is ways to apply an adaptive approach to information security management. The scientific novelty of the obtained results is determined by the proposed method of calculating the acceptable (effective) volume of investments in the information security system, the structure and functions of which are formed on the basis of the principle of adaptive management of the organization's IB. The practical significance of the results obtained is the ability to apply the proposed method to calculate the effective amount of investment in an organization's information security system

    Classification and definition of geometric parameters of compartments of complex structural body models

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    For a safety estimation of complex structural body models, definition of geometric parameters such as a volume and center of volume is essential. Thus, it should change modeling information of face structure to B-rep or CSG form for definition of geometric parameters through each section categorization on the commercial CAD systems. But, complex structural body models can cause error while it is transformed to B-rep or CSG or too much time is spent for each face selection. And, CAD system using in almost ship yard is uncomfortable to input offset information as manual to define for each section volume, geometric parameters and then that CAD system is outside program which is paying license fee every year. Therefore, this research has developed automatic system which calculate geometric parameters each section and classify each section of complex structural body models without limitation for solid modeling and section information.1. 서 론 1 2. 개발 도구 2.1 개요 4 2.2 기하학적 모델 4 2.3 NURBS 모델링 5 2.4 IGES 8 2.5 Open CASCADE 10 2.5.1 개요 10 2.5.2 구조 15 3. 알고리즘 개발 3.1 개요 18 3.2 모델링 정보 입출력 21 3.3 모델링 객체 절단 24 3.4 절단된 단면 위의 선 분할 26 3.5 겹치는 선 및 돌출된 선 제거 29 3.6 구획을 구성하는 선 그룹화 30 3.7 구획을 구성하는 선 그룹의 반시계방향 확인 33 3.8 구획을 구성하는 선 그룹의 면 변환 36 3.9 구획을 구성하는 단면간의 연결성 확인 38 3.10 구획의 기하학적 요소 39 4. 결 론 48 참고문헌 4

    On environment difficulty and discriminating power

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-014-9257-1This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multiagent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multiagent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.I thank the reviewers for their comments, especially those aiming at a clearer connection with the field of multi-agent systems and the suggestion of better approximations for the calculation of the response curves. The implementation of the elementary cellular automata used in the environments is based on the library 'CellularAutomaton' by John Hughes for R [58]. I am grateful to Fernando Soler-Toscano for letting me know about their work [65] on the complexity of 2D objects generated by elementary cellular automata. I would also like to thank David L. Dowe for his comments on a previous version of this paper. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).José Hernández-Orallo (2015). On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems. 29(3):402-454. https://doi.org/10.1007/s10458-014-9257-1S402454293Anderson, J., Baltes, J., & Cheng, C. T. (2011). Robotics competitions as benchmarks for ai research. The Knowledge Engineering Review, 26(01), 11–17.Andre, D., & Russell, S. J. (2002). State abstraction for programmable reinforcement learning agents. In Proceedings of the National Conference on Artificial Intelligence (pp. 119–125). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.Antunes, L., Fortnow, L., van Melkebeek, D., & Vinodchandran, N. V. (2006). Computational depth: Concept and applications. Theoretical Computer Science, 354(3), 391–404. Foundations of Computation Theory (FCT 2003), 14th Symposium on Fundamentals of Computation Theory 2003.Arai, K., Kaminka, G. A., Frank, I., & Tanaka-Ishii, K. (2003). Performance competitions as research infrastructure: Large scale comparative studies of multi-agent teams. Autonomous Agents and Multi-Agent Systems, 7(1–2), 121–144.Ashcraft, M. H., Donley, R. D., Halas, M. A., & Vakali, M. (1992). Chapter 8 working memory, automaticity, and problem difficulty. In Jamie I.D. Campbell (Ed.), The nature and origins of mathematical skills, volume 91 of advances in psychology (pp. 301–329). North-Holland.Ay, N., Müller, M., & Szkola, A. (2010). Effective complexity and its relation to logical depth. IEEE Transactions on Information Theory, 56(9), 4593–4607.Barch, D. M., Braver, T. S., Nystrom, L. E., Forman, S. D., Noll, D. C., & Cohen, J. D. (1997). Dissociating working memory from task difficulty in human prefrontal cortex. Neuropsychologia, 35(10), 1373–1380.Bordini, R. H., Hübner, J. F., & Wooldridge, M. (2007). Programming multi-agent systems in AgentSpeak using Jason. London: Wiley. com.Boutilier, C., Reiter, R., Soutchanski, M., Thrun, S. et al. (2000). Decision-theoretic, high-level agent programming in the situation calculus. In Proceedings of the National Conference on Artificial Intelligence (pp. 355–362). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.Busoniu, L., Babuska, R., & De Schutter, B. (2008). A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 38(2), 156–172.Chaitin, G. J. (1977). Algorithmic information theory. IBM Journal of Research and Development, 21, 350–359.Chedid, F. B. (2010). Sophistication and logical depth revisited. In 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) (pp. 1–4). IEEE.Cheeseman, P., Kanefsky, B. & Taylor, W. M. (1991). Where the really hard problems are. In Proceedings of IJCAI-1991 (pp. 331–337).Dastani, M. (2008). 2APL: A practical agent programming language. Autonomous Agents and Multi-agent Systems, 16(3), 214–248.Delahaye, J. P. & Zenil, H. (2011). Numerical evaluation of algorithmic complexity for short strings: A glance into the innermost structure of randomness. Applied Mathematics and Computation, 219(1), 63–77Dowe, D. L. (2008). Foreword re C. S. Wallace. Computer Journal, 51(5), 523–560. Christopher Stewart WALLACE (1933–2004) memorial special issue.Dowe, D. L., & Hernández-Orallo, J. (2012). IQ tests are not for machines, yet. Intelligence, 40(2), 77–81.Du, D. Z., & Ko, K. I. (2011). Theory of computational complexity (Vol. 58). London: Wiley-Interscience.Elo, A. E. (1978). The rating of chessplayers, past and present (Vol. 3). London: Batsford.Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. London: Lawrence Erlbaum.Fatès, N. & Chevrier, V. (2010). How important are updating schemes in multi-agent systems? an illustration on a multi-turmite model. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1-Volume 1 (pp. 533–540). International Foundation for Autonomous Agents and Multiagent Systems.Ferber, J. & Müller, J. P. (1996). Influences and reaction: A model of situated multiagent systems. In Proceedings of Second International Conference on Multi-Agent Systems (ICMAS-96) (pp. 72–79).Ferrando, P. J. (2009). Difficulty, discrimination, and information indices in the linear factor analysis model for continuous item responses. Applied Psychological Measurement, 33(1), 9–24.Ferrando, P. J. (2012). Assessing the discriminating power of item and test scores in the linear factor-analysis model. Psicológica, 33, 111–139.Gent, I. P., & Walsh, T. (1994). Easy problems are sometimes hard. Artificial Intelligence, 70(1), 335–345.Gershenson, C. & Fernandez, N. (2012). Complexity and information: Measuring emergence, self-organization, and homeostasis at multiple scales. Complexity, 18(2), 29–44.Gruner, S. (2010). Mobile agent systems and cellular automata. Autonomous Agents and Multi-agent Systems, 20(2), 198–233.Hardman, D. K., & Payne, S. J. (1995). Problem difficulty and response format in syllogistic reasoning. The Quarterly Journal of Experimental Psychology, 48(4), 945–975.He, J., Reeves, C., Witt, C., & Yao, X. (2007). A note on problem difficulty measures in black-box optimization: Classification, realizations and predictability. Evolutionary Computation, 15(4), 435–443.Hernández-Orallo, J. (2000). Beyond the turing test. Journal of Logic Language & Information, 9(4), 447–466.Hernández-Orallo, J. (2000). On the computational measurement of intelligence factors. In A. Meystel (Ed.), Performance metrics for intelligent systems workshop (pp. 1–8). Gaithersburg, MD: National Institute of Standards and Technology.Hernández-Orallo, J. (2000). Thesis: Computational measures of information gain and reinforcement in inference processes. AI Communications, 13(1), 49–50.Hernández-Orallo, J. (2010). A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In M. Hutter et al. (Ed.), 3rd International Conference on Artificial General Intelligence (pp. 182–183). Atlantis Press Extended report at http://users.dsic.upv.es/proy/anynt/unbiased.pdf .Hernández-Orallo, J., & Dowe, D. L. (2010). Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence, 174(18), 1508–1539.Hernández-Orallo, J., Dowe, D. L., España-Cubillo, S., Hernández-Lloreda, M. V., & Insa-Cabrera, J. (2011). On more realistic environment distributions for defining, evaluating and developing intelligence. In J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), LNAI series on artificial general intelligence 2011 (Vol. 6830, pp. 82–91). Berlin: Springer.Hernández-Orallo, J., Dowe, D. L., & Hernández-Lloreda, M. V. (2014). Universal psychometrics: Measuring cognitive abilities in the machine kingdom. Cognitive Systems Research, 27, 50–74.Hernández-Orallo, J., Insa, J., Dowe, D. L. & Hibbard, B. (2012). Turing tests with turing machines. In A. Voronkov (Ed.), The Alan Turing Centenary Conference, Turing-100, Manchester, 2012, volume 10 of EPiC Series (pp. 140–156).Hernández-Orallo, J. & Minaya-Collado, N. (1998). A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In Proceedings of International Symposium of Engineering of Intelligent Systems (EIS’98) (pp. 146–163). ICSC Press.Hibbard, B. (2009). Bias and no free lunch in formal measures of intelligence. Journal of Artificial General Intelligence, 1(1), 54–61.Hoos, H. H. (1999). Sat-encodings, search space structure, and local search performance. In 1999 International Joint Conference on Artificial Intelligence (Vol. 16, pp. 296–303).Insa-Cabrera, J., Benacloch-Ayuso, J. L., & Hernández-Orallo, J. (2012). On measuring social intelligence: Experiments on competition and cooperation. In J. Bach, B. Goertzel, & M. Iklé (Eds.), AGI, volume 7716 of lecture notes in computer science (pp. 126–135). Berlin: Springer.Insa-Cabrera, J., Dowe, D. L., España-Cubillo, S., Hernández-Lloreda, M. V., & Hernández-Orallo, J. (2011). Comparing humans and AI agents. In J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), LNAI series on artificial general intelligence 2011 (Vol. 6830, pp. 122–132). Berlin: Springer.Knuth, D. E. (1973). Sorting and searching, volume 3 of the art of computer programming. Reading, MA: Addison-Wesley.Kotovsky, K., & Simon, H. A. (1990). What makes some problems really hard: Explorations in the problem space of difficulty. Cognitive Psychology, 22(2), 143–183.Legg, S. (2008). Machine super intelligence. PhD thesis, Department of Informatics, University of Lugano, June 2008.Legg, S., & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.Leonetti, M. & Iocchi, L. (2010). Improving the performance of complex agent plans through reinforcement learning. In Proceedings of the 2010 International Conference on Autonomous Agents and Multiagent Systems (Vol. 1, pp. 723–730). International Foundation for Autonomous Agents and Multiagent Systems.Levin, L. A. (1973). Universal sequential search problems. Problems of Information Transmission, 9(3), 265–266.Levin, L. A. (1986). Average case complete problems. SIAM Journal on Computing, 15, 285.Li, M., & Vitányi, P. (2008). An introduction to Kolmogorov complexity and its applications (3rd ed.). Berlin: Springer.Low, C. K., Chen, T. Y., & Rónnquist, R. (1999). Automated test case generation for bdi agents. Autonomous Agents and Multi-agent Systems, 2(4), 311–332.Madden, M. G., & Howley, T. (2004). Transfer of experience between reinforcement learning environments with progressive difficulty. Artificial Intelligence Review, 21(3), 375–398.Mellenbergh, G. J. (1994). Generalized linear item response theory. Psychological Bulletin, 115(2), 300.Michel, F. (2004). Formalisme, outils et éléments méthodologiques pour la modélisation et la simulation multi-agents. PhD thesis, Université des sciences et techniques du Languedoc, Montpellier.Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.Orponen, P., Ko, K. I., Schöning, U., & Watanabe, O. (1994). Instance complexity. Journal of the ACM (JACM), 41(1), 96–121.Simon, H. A., & Kotovsky, K. (1963). Human acquisition of concepts for sequential patterns. Psychological Review, 70(6), 534.Team, R., et al. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Whiteson, S., Tanner, B., & White, A. (2010). The reinforcement learning competitions. The AI Magazine, 31(2), 81–94.Wiering, M., & van Otterlo, M. (Eds.). (2012). Reinforcement learning: State-of-the-art. Berlin: Springer.Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.Zatuchna, Z., & Bagnall, A. (2009). Learning mazes with aliasing states: An LCS algorithm with associative perception. Adaptive Behavior, 17(1), 28–57.Zenil, H. (2010). Compression-based investigation of the dynamical properties of cellular automata and other systems. Complex Systems, 19(1), 1–28.Zenil, H. (2011). Une approche expérimentale à la théorie algorithmique de la complexité. PhD thesis, Dissertation in fulfilment of the degree of Doctor in Computer Science, Université de Lille.Zenil, H., Soler-Toscano, F., Delahaye, J. P. & Gauvrit, N. (2012). Two-dimensional kolmogorov complexity and validation of the coding theorem method by compressibility. arXiv, preprint arXiv:1212.6745
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