C-tests revisited: back and forth with complexity

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-21365-1_28We explore the aggregation of tasks by weighting them using a difficulty function that depends on the complexity of the (acceptable) policy for the task (instead of a universal distribution over tasks or an adaptive test). The resulting aggregations and decompositions are (now retrospectively) seen as the natural (and trivial) interactive generalisation of the C-tests.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2010-21062-C02-02, PCIN-2013-037 and TIN 2013-45732-C4-1-P, and by Generalitat Valenciana PROMETEOII 2015/013.Hernández Orallo, J. (2015). C-tests revisited: back and forth with complexity. En Artificial General Intelligence 8th International Conference, AGI 2015, AGI 2015, Berlin, Germany, July 22-25, 2015, Proceedings. Springer International Publishing. 272-282. https://doi.org/10.1007/978-3-319-21365-1_28S272282Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research 47, 253–279 (2013)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: Computational measures of information gain and reinforcement in inference processes. AI Communications 13(1), 49–50 (2000)Hernández-Orallo, J.: On the computational measurement of intelligence factors. In: Meystel, A. (ed.) Performance metrics for intelligent systems workshop, pp. 1–8. National Institute of Standards and Technology, Gaithersburg (2000)Hernández-Orallo, J.: AI evaluation: past, present and future (2014). arXiv preprint arXiv:1408.6908Hernández-Orallo, J.: On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems, 1–53 (2014). http://dx.doi.org/10.1007/s10458-014-9257-1Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Hernández-Orallo, J., Dowe, D.L., Hernández-Lloreda, M.V.: Universal psychometrics: Measuring cognitive abilities in the machine kingdom. Cognitive Systems Research 27, 50–74 (2014)Hernández-Orallo, J., Minaya-Collado, N.: A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proc. Intl. Symposium of Engineering of Intelligent Systems (EIS 1998), pp. 146–163. ICSC Press (1998)Hibbard, B.: Bias and no free lunch in formal measures of intelligence. Journal of Artificial General Intelligence 1(1), 54–61 (2009)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Li, M., Vitányi, P.: An introduction to Kolmogorov complexity and its applications, 3 edn. Springer-Verlag (2008)Schaul, T.: An extensible description language for video games. IEEE Transactions on Computational Intelligence and AI in Games PP(99), 1–1 (2014)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and control 7(1), 1–22 (1964

Compression and intelligence: social environments and communication

Compression has been advocated as one of the principles which pervades inductive inference and prediction - and, from there, it has also been recurrent in definitions and tests of intelligence. However, this connection is less explicit in new approaches to intelligence. In this paper, we advocate that the notion of compression can appear again in definitions and tests of intelligence through the concepts of `mind-reading¿ and `communication¿ in the context of multi-agent systems and social environments. Our main position is that two-part Minimum Message Length (MML) compression is not only more natural and effective for agents with limited resources, but it is also much more appropriate for agents in (co-operative) social environments than one-part compression schemes - particularly those using a posterior-weighted mixture of all available models following Solomonoff¿s theory of prediction. We think that the realisation of these differences is important to avoid a naive view of `intelligence as compression¿ in favour of a better understanding of how, why and where (one-part or two-part, lossless or lossy) compression is needed.We thank the anonymous reviewers for their helpful comments, and we thank Kurt Kleiner for some challenging and ultimately very helpful questions in the broad area of this work. We also acknowledge the funding from the Spanish MEC and MICINN for projects TIN2009-06078-E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062-C02, and Generalitat Valenciana for Prometeo/2008/051.Dowe, DL.; Hernández Orallo, J.; Das, PK. (2011). Compression and intelligence: social environments and communication. En Artificial General Intelligence. Springer Verlag (Germany). 6830:204-211. https://doi.org/10.1007/978-3-642-22887-2_21S2042116830Chaitin, G.J.: Godel’s theorem and information. International Journal of Theoretical Physics 21(12), 941–954 (1982)Dowe, D.L.: Foreword re C. S. Wallace. Computer Journal 51(5), 523–560 (2008); Christopher Stewart WALLACE (1933-2004) memorial special issueDowe, D.L.: Minimum Message Length and statistically consistent invariant (objective?) Bayesian probabilistic inference - from (medical) “evidence”. Social Epistemology 22(4), 433–460 (2008)Dowe, D.L.: MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness. In: Bandyopadhyay, P.S., Forster, M.R. (eds.) Handbook of the Philosophy of Science. Philosophy of Statistics, vol. 7, pp. 901–982. Elsevier, Amsterdam (2011)Dowe, D.L., Hajek, A.R.: A computational extension to the Turing Test. Technical Report #97/322, Dept Computer Science, Monash University, Melbourne, Australia, 9 pp (1997)Dowe, D.L., Hajek, A.R.: A non-behavioural, computational extension to the Turing Test. In: Intl. Conf. on Computational Intelligence & multimedia applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (February 1998)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: Constructive reinforcement learning. International Journal of Intelligent Systems 15(3), 241–264 (2000)Hernández-Orallo, J.: On the computational measurement of intelligence factors. In: Meystel, A. (ed.) Performance metrics for intelligent systems workshop, pp. 1–8. National Institute of Standards and Technology, Gaithersburg, MD, U.S.A (2000)Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Hernández-Orallo, J., Minaya-Collado, N.: A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proc. Intl Symposium of Engineering of Intelligent Systems (EIS 1998), pp. 146–163. 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Journal of Artificial Intelligence Research, JAIR 40, 95–142 (2011)Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length. Springer, Heidelberg (2005)Wallace, C.S., Boulton, D.M.: An information measure for classification. Computer Journal 11(2), 185–194 (1968)Wallace, C.S., Dowe, D.L.: Intrinsic classification by MML - the Snob program. In: Proc. 7th Australian Joint Conf. on Artificial Intelligence, pp. 37–44. World Scientific, Singapore (November 1994)Wallace, C.S., Dowe, D.L.: Minimum message length and Kolmogorov complexity. Computer Journal 42(4), 270–283 (1999); Special issue on Kolmogorov complexityWallace, C.S., Dowe, D.L.: MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing 10, 73–83 (2000

A study on non-destructive method for detecting Toxin in pepper using Neural networks

Mycotoxin contamination in certain agricultural systems have been a serious concern for human and animal health. Mycotoxins are toxic substances produced mostly as secondary metabolites by fungi that grow on seeds and feed in the field, or in storage. The food-borne Mycotoxins likely to be of greatest significance for human health in tropical developing countries are Aflatoxins and Fumonisins. Chili pepper is also prone to Aflatoxin contamination during harvesting, production and storage periods.Various methods used for detection of Mycotoxins give accurate results, but they are slow, expensive and destructive. Destructive method is testing a material that degrades the sample under investigation. Whereas, non-destructive testing will, after testing, allow the part to be used for its intended purpose. Ultrasonic methods, Multispectral image processing methods, Terahertz methods, X-ray and Thermography have been very popular in nondestructive testing and characterization of materials and health monitoring. Image processing methods are used to improve the visual quality of the pictures and to extract useful information from them. In this proposed work, the chili pepper samples will be collected, and the X-ray, multispectral images of the samples will be processed using image processing methods. The term "Computational Intelligence" referred as simulation of human intelligence on computers. It is also called as "Artificial Intelligence" (AI) approach. The techniques used in AI approach are Neural network, Fuzzy logic and evolutionary computation. Finally, the computational intelligence method will be used in addition to image processing to provide best, high performance and accurate results for detecting the Mycotoxin level in the samples collected.Comment: 11 pages,1 figure; International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.4, July 201

On potential cognitive abilities in the machine kingdom

The final publication is available at Springer via http://dx.doi.org/10.1007/s11023-012-9299-6Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the ‘machine kingdom’. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as ‘universal psychometrics’, a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent.We thank the anonymous reviewers for their comments, which have helped to significantly improve 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. Finally, we thank three pioneers ahead of their time(s). We thank Ray Solomonoff (1926-2009) and Chris Wallace (1933-2004) for all that they taught us, directly and indirectly. And, in his centenary year, we thank Alan Turing (1912-1954), with whom it perhaps all began.Hernández-Orallo, J.; Dowe, DL. (2013). On potential cognitive abilities in the machine kingdom. Minds and Machines. 23(2):179-210. https://doi.org/10.1007/s11023-012-9299-6S179210232Amari, S., Fujita, N., Shinomoto, S. (1992). Four types of learning curves. Neural Computation 4(4), 605–618.Aristotle (Translation, Introduction, and Commentary by Ross, W.D.) (1924). Aristotle’s Metaphysics. Oxford: Clarendon Press.Barmpalias, G. & Dowe, D. L. (2012). 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Forster (Eds), Handbook of the philosophy of science—Volume 7: Philosophy of statistics (pp. 901–982). Amsterdam: Elsevier.Dowe, D. L. & Hajek, A. R. (1997a). A computational extension to the turing test. Technical report #97/322, Dept Computer Science, Monash University, Melbourne, Australia, 9 pp, http://www.csse.monash.edu.au/publications/1997/tr-cs97-322-abs.html .Dowe, D. L. & Hajek, A. R. (1997b, September). A computational extension to the Turing Test. in Proceedings of the 4th conference of the Australasian Cognitive Science Society, University of Newcastle, NSW, Australia, 9 pp.Dowe, D. L. & Hajek, A. R. (1998, February). A non-behavioural, computational extension to the Turing Test. In: International conference on computational intelligence and multimedia applications (ICCIMA’98), Gippsland, Australia, pp 101–106.Dowe, D. L., Hernández-Orallo, J. (2012). IQ tests are not for machines, yet. Intelligence, 40(2), 77–81.Gallistel, C. R., Fairhurst, S., & Balsam, P. (2004). 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(Eds.), Proceedings of 3rd international conference on artificial general intelligence (pp. 25–30). New York: Atlantis Press.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. (2011, April). Mammals, machines and mind games. Who’s the smartest?. The conversation, http://theconversation.edu.au/mammals-machines-and-mind-games-whos-the-smartest-566 .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.), Artificial general intelligence 2011, volume 6830, LNAI series, pp. 82–91. New York: Springer.Hernández-Orallo, J., Dowe, D. L., & Hernández-Lloreda, M. V. (2012a, March). 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An Ontological-based Knowledge-Representation Formalism for Case-Based Argumentation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-014-9524-3[EN] In open multi-agent systems, agents can enter or leave the system, interact, form societies, and have dependency relations with each other. In these systems, when agents have to collaborate or coordinate their activities to achieve their objectives, their different interests and preferences can come into conflict. Argumentation is a powerful technique to harmonise these conflicts. However, in many situations the social context of agents determines the way in which agents can argue to reach agreements. In this paper, we advance research in the computational representation of argumentation frameworks by proposing a new ontologicalbased, knowledge-representation formalism for the design of open MAS in which the participating software agents are able to manage and exchange arguments with each other taking into account the agents’ social context. This formalism is the core of a case-based argumentation framework for agent societies. In addition, we present an example of the performance of the formalism in a real domain that manages the requests received by the technicians of a call centre.This work is supported by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO II/2013/019].Heras Barberá, SM.; Botti, V.; Julian Inglada, VJ. (2014). An Ontological-based Knowledge-Representation Formalism for Case-Based Argumentation. Information Systems Frontiers. 1-20. https://doi.org/10.1007/s10796-014-9524-3S120Amgoud, L. (2005). An argumentation-based model for reasoning about coalition structures. In 2nd international workshop on argumentation in multi-agent systems, argmas-05(pp. 1–12). Springer.Amgoud, L., Dimopolous, Y., Moraitis, P. (2007). A unified and general framework for argumentation-based negotiation. In 6th international joint conference on autonomous agents and multiagent systems, AAMAS-07. IFAAMAS.Atkinson, K., & Bench-Capon, T. (2008). Abstract argumentation scheme frameworks. In Proceedings of the 13th international conference on artificial intelligence: methodology, systems and applications, AIMSA-08, lecture notes in artificial intelligence (Vol. 5253, pp. 220–234). Springer.Aulinas, M., Tolchinsky, P., Turon, C., Poch, M., Cortés, U. (2012). Argumentation-based framework for industrial wastewater discharges management. Engineering Applications of Artificial Intelligence, 25(2), 317–325.Bench-Capon, T., & Atkinson, K. (2009). Argumentation in artificial intelligence, chap. abstract argumentation and values (pp. 45–64). Springer.Bench-Capon, T., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150(1-2), 97–143.Bulling, N., Dix, J., Chesñevar, C.I. (2008). Modelling coalitions: ATL + argumentation. In Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, AAMAS-08 (Vol. 2, pp. 681–688). ACM Press.Chesñevar, C., McGinnis, J., Modgil, S., Rahwan, I., Reed, C., Simari, G., South, M., Vreeswijk, G., Willmott, S. (2006). Towards an argument interchange format. The Knowledge Engineering Review, 21(4), 293–316.Diaz-Agudo, B., & Gonzalez-Calero, P.A. (2007). Ontologies: A handbook of principles, concepts and applications in information systems, integrated series in information systems, chap. an ontological approach to develop knowledge intensive cbr systems (Vol. 14, pp. 173–214). Springer.Dung, P.M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and N -person games. Artificial Intelligence, 77, 321–357.Ferber, J., Gutknecht, O., Michel, F. (2004). From agents to organizations: An organizational view of multi-agent systems. In Agent-oriented software engineering VI, LNCS (Vol. 2935, pp. 214–230.) Springer-Verlag.Hadidi, N., Dimopolous, Y., Moraitis, P. (2010). Argumentative alternating offers. In 9th international conference on autonomous agents and multiagent systems, AAMAS-10 (pp. 441–448). IFAAMAS.Heras, S., Atkinson, K., Botti, V., Grasso, F., Julián, V., McBurney, P. (2010). How argumentation can enhance dialogues in social networks. In Proceedings of the 3rd international conference on computational models of argument, COMMA-10, frontiers in artificial intelligence and applications (Vol. 216, pp. 267–274). IOS Press.Heras, S., Botti, V., Julián, V. (2011). On a computational argumentation framework for agent societies. In Argumentation in multi-agent systems (pp. 123–140). Springer.Heras, S., Botti, V., Julián, V. (2012). Argument-based agreements in agent societies. Neurocomputing, 75(1), 156–162.Heras, S., Jordán, J., Botti, V., Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82–108.Jordán, J., Heras, S., Julián, V. (2011). A customer support application using argumentation in multi-agent systems. In 14th international conference on information fusion (FUSION-11) (pp. 772– 778).Karunatillake, N.C. (2006). Argumentation-based negotiation in a social context. Ph.D. thesis, School of Electronics and Computer Science, University of Southampton, UK.Karunatillake, N.C., Jennings, N.R., Rahwan, I., McBurney, P. (2009). Dialogue games that agents play within a society. Artificial Intelligence, 173(9-10), 935–981.Kraus, S., Sycara, K., Evenchik, A. (1998). Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 104, 1–69.López de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M., Forbus, K., Keane, M., Watson, I. (2006). 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