Abstract
In the present paper it is proposed that Machine Consciousness can be implemented by using either Finite State Automata or Production Systems. In both cases a possible behavior that may be characterized as exhibiting consciousness is the generation of an explanation of how it generates its final output. The implementation of Machine Consciousness techniques as applied to the technology of Question Answering is illustrated with our AMYNTAS Deductive Question Answering system. This system is described and it is shown how it generates in addition to an answer to the input question an explanatory report in natural language of the steps followed by the computation for the generation of an answer. Our implemented system is based both on finite state automata and on production systems and generates explanations in two ways while Question Answering from texts. One way is based on the state change path followed by an automaton and the other is based on the chain of productions activated during generating an answer. Our system was evaluated for precision and recall with a biologist as judge for information extraction from biological texts as well as for flexibility by showing that it can easily be adapted to three new domains. In contrast to our AMYNTAS system two prize winning programs at the Turing test Loebner competition that we tested failed to exhibit comparable performance as shown by the dialog trace of the tests presented here.

Keywords
Machine Consciousness , Question Answering , Explanation, Deductive

Cite this paper
John Kontos, Machine Consciousness and Question Answering , SCIREA Journal of Computer. Volume 1, Issue 1, October 2016 | PP. 58-80.

References

[ 1 ]	Wright M.T. (2007). "The Antikythera Mechanism Reconsidered." Interdisciplinary Science Reviews 32, 27-43.
[ 2 ]	Freeth T. and A. Jones (2012).“The Cosmos in the Antikythera Mechanism.” ISAW Papers 4, February.
[ 3 ]	Wikipedia, the free encyclopedia (2014). “Antikythera Mechanism.”
[ 4 ]	Baars, B. J. (2003), "The Global Brainweb: An Update on Global Workspace Theory", Science and Consciousness Review (October).
[ 5 ]	Aru J. and T. Bachmann (2015). “Still wanted-the mechanisms of consciousness!” Frontiers in Psychology, Vol 6, January.
[ 6 ]	Webb TW, Graziano MSA (2015) “The attention schema theory: a mechanistic account of subjective awareness”. Frontiers in Psychology,23d April.
[ 7 ]	Graziano MSA, Webb TW (2014) “A mechanistic theory of consciousness.” International Journal of Machine Consciousness, Vol 6, No2.
[ 8 ]	Gelepithis P. (2014). “A Novel Theory of Consciousness.” International Journal of Machine Consciousness Vol 6 No 2, 125-139.
[ 9 ]	Wikipedia, the free encyclopedia (2016). “ Artificial Consciousness”.
[ 10 ]	Schubert, L. (2005). “Some knowledge representation for Self-Awareness.” Metacognition in Computation, 106-113.
[ 11 ]	Shapiro, S. C. et al (2007). “Metacognition in SnePS.” AI Magazine 28,1,Spring, 17-31.
[ 12 ]	Sloman A. (2011). “Varieties of Metacognition in Natural and Artificial Systems.” Metareasoning: Thinking about Thinking. Edited by M.T. Cox and A. Raja. MIT Press, Cambridge, Massachussets.
[ 13 ]	Gamez, David (2008). "Progress in machine consciousness", Consciousness and Cognition 17, 887–910.
[ 14 ]	Arrabales, Raul (2009). "Establishing a Roadmap and Metrics for Conscious Machines Development",Proceedings of the 8th IEEE International Conference on Cognitive Informatics (Hong Kong), 94–101.
[ 15 ]	Chalmers, David (2011). "A Computational Foundation for the Study of Cognition", Journal of Cognitive Science ,323–357.
[ 16 ]	Starzyk J. A. and D. K. Prasad (2011). “A Computational Model of Machine Consciousness.” Int. J. Machine Consciousness, 03, 255.
[ 17 ]	Alexander I. and H. Morton (2012). “Aristotle’s Laptop” Vol. 1 of the Series on Machine Consciousness, World Scientific.
[ 18 ]	Manzotti, R. (2013). “Machine Consciousness: A Modern Approach.” in Natural Intelligence: the INNS Magazine, 2(1), 7–18.
[ 19 ]	Reggia, James (2013). "The rise of machine consciousness: Studying consciousness with computational models", Neural Networks 44, 112–131.
[ 20 ]	Gamez D. (2014). “The measurement of consciousness: a framework for the scientific study of consciousness” Frontiers in Psychology 5, 714.
[ 21 ]	Goertzel B. (2014); “Characterizing Human-like Consciousness: An Integrative Approach.” Procedia Computer Science, Volume 41, 152–157, 5th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA.
[ 22 ]	Reggia J. A. (2014). “Conscious Machines: The AI Perspective.” AAAI Fall Symposium Series, North America, September.
[ 23 ]	Khalid F. (2014) “How Artificial Intelligence Can Inform Neuroscience: A Recipe for Conscious Machines?” Santa Fe Institute 2014 Complex Systems Summer School Proceeding
[ 24 ]	Bringsjord S. et al (2015) “Real robots that pass human tests of self-consciousness.” RO-MAN 2015, 498-504.
[ 25 ]	Paraense, A. L. O., et al (2016). “A machine consciousness approach to urban traffic control.” Biologically Inspired Cognitive Architectures, Volume 15, January 2016, 61-73.
[ 26 ]	Turing A. (1950). “Computing Machinery and Intelligence.” Mind LlX, October, No 236, 433-460.
[ 27 ]	Kontos J. and P. Kasda (2015). “ Artificial Intelligence Professor John Kontos needles Poly Kasda’s Conscious Eye” . Notios Anemos , Athens.
[ 28 ]	Kontos J. (1985). “Natural Language Processing of Scientific/Technical Data, Knowledge and Text Bases.” (Invited paper) Proceedings of the EEC ARTINT (Artificial Intelligence) Workshop, Luxemburg.
[ 29 ]	Kontos J. (1992). “ARISTA: Knowledge Engineering with Scientific Texts.” Information and Software Technology, Vol. 34, No 9, 611-616.
[ 30 ]	Kontos J. et al (2002). “ARISTA Causal Knowledge Discovery from Texts.” Proceedings of the 5th International Conference on Discovery Science DS 2002, Luebeck, Germany, 348-355.
[ 31 ]	Kontos J. et al (2005). “Question answering and rhetoric analysis of biomedical texts in the AROMA system.” In Proceedings of the 7th Hellenic Europeoan Conference on ComputerMathematics and its Applications.
[ 32 ]	Kontos J. and I. Malagardi, (2006). “Question Answering from Procedural Semantics to Model Discovery.” In Encyclopedia of Human Computer Interaction. C. Ghaoui, (Ed.). Idea Group Publishing Company.
[ 33 ]	Kontos J. and J. Armaos (2007), “Metacognitive Question Answering from Euclid’s Elements Text.” HERCMA 2007, Athens.
[ 34 ]	Kontos J et al, (2009) “Metagnostic Question Answering from Biomedical Texts.” HCI2009, San Diego, USA, July 19-24.
[ 35 ]	Kontos J. et al (2011). “Metagnostic Deductive Question Answering with Explanation from Texts”, Universal Access in HCI, Part IV, HCII 2011, 72–80, Springer-Verlag Berlin Heidelberg.
[ 36 ]	Kontos J. et al. (2012). “Metagnostic Information Extraction from Historic Texts.” Studies in Greek Linguistics, published by the Institute of Modern Greek Studies, Thessaloniki.
[ 37 ]	Kontos J. and P. Kasda (2013). “Text Mining and Image Anomaly Explanation with Machine Consciousness.” Advances in Computer Science, Vol. 2, Issue 5, No.6, November.
[ 38 ]	Athenikos S. J. and Hyoil Han. (2010). “Biomedical question answering: A survey.” Computer Methods and Programs in Biomedicine, 99(1),1 – 24.
[ 39 ]	Simpson M. N and D. Demner-Fushman (2012). “Biomedical Text Mining: A Survey of Recent Progress.” Mining Text Data 2012, 465-517.
[ 40 ]	Meadows, B., Heald, R., & Langley, P. (2015). “An Integrated Account of Explanation and Question Answering.” Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 1571-1576. California, United States.
[ 41 ]	Mishra A. and Sanjay Kumar Jain (2015). “A survey on question answering systems with classification.” In Press, Journal of King Saud University - Computer and Information Sciences. November.
[ 42 ]	Heath T. L. (1956). “The Thirteen books of Euclid’s Elements.” Dover..
[ 43 ]	Aleven, V., et al (2006). “Toward Meta-cognitive Tutoring: A Model of Help-Seeking with a Cognitive Tutor. “International Journal of Artificial Intelligence in Education 16, 101-130.
[ 44 ]	Chi, M., VanLehn, K. (2008). “Eliminating the gap between the high and low students through meta-cognitive strategy instructions.” in B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds.). Proceedings of the 9th International Conference on Intelligent Tutoring Systems. Intelligence 169 104-141.
[ 45 ]	Paris, S. G. and Winograd, P. (1990). “How metacognition can promote academic learning and instruction.” B. F. Jones and L. Idol (Eds) Dimensions of Thinking and Cognitive Instruction (Hillsdale, NJ, Lawrence Erlbaum), 15–51.
[ 46 ]	Veenman, M. V. J. et al. (2006). “Metacognition and learning: conceptual and methodological considerations.” Metacognition 1, 3-14.
[ 47 ]	Zion, M. et al (2005). “The effects of metacognitive instruction embedded within an asynchronous learning network on scientific inquiry skills.” International Journal of Science Education, vol.27, No.8, 957-983.
[ 48 ]	Harbers M. et al (2009). “A Methodology for Developing Self-Explaining Agents for Virtual Training.” In M. Dastani, A. Fallah Seghrouchni, J. Leite, and P. Torroni (Eds.) Proc. of 2nd Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW'009), Turin, Italy.
[ 49 ]	Harbers M. et al (2011). “Explanation and Coordination in Human-Agent Teams: A Study in the BW4T Testbed.” Proceedings of the COIN workshop, 17-20.
[ 50 ]	Lomas M. et al (2012) “Explaining robot actions.” HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction 187-188 ACM New York, NY, USA.
[ 51 ]	Licato J. et al (2015). “Modeling the Creation and Development of Cause-Effect Pairs for Explanation Generation in a Cognitive Architecture.” AIC 2015, 29-39.
[ 52 ]	Wang, N., et al (2015). “Building Trust in a Human-Robot Team with Automatically Generated Explanations.” Proceedings of the Interservice/Industry Training, Simulation and Education Conference (I/ITSEC).
[ 53 ]	Wang, N. et al (2016). “Trust Calibration within a Human-Robot Team: Comparing Automatically Generated Explanations.” Proceeding of the 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).