Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution: Applied in Cecilia: An Architecture of a Digital Companion Artificial Intelligence (AI) Agent System Composer of Dialogue Scripts for Well-Being and Mental Health
Pages 42 - 71
Abstract
Contribution of this work is to Define the Creative Composition Problem (CCP) for Human Well-being Optimization by Construction of Knowledge Graph using Knowledge Representation and logic-based Artificial Intelligence reasoning-planning where the computation of the Optimal Solution is achieved by Dynamic Programming or Logic Programming. The Creative Composition Problem is embedded within Cecilia: an architecture of a digital companion artificial intelligence agent system composer of dialogue scripts for Well-being and Mental Health. Where Cecilia Framework is instantiated in Well-being and Mental Health domain for optimal well-being development of first year university students. We define the ‘The Problem of Creating a Dialogue Composition (PCDC)’ and we propose a feasible and optimal solution of it. CCP is instantiated in this applied domain to solve PCDC optimizing the Mental Health and Well-being of the student. CCP as PCDC is applied to optimize maximizing the mental health of the student but also maximizing the smoothness, coherence, enjoyment and engagement each time the dialogue session is composed. Cecilia helps students to manage stress/anxiety to attempt the prevention of depression. Students can interact through the digital companion making questions and answers. While the system “learns” from the user it allows the user to learn from herself. Once the student discovers elements that were unnoticed by her, she will find a better way to improve when discovering her points of improvement.
References
[1]
Arias, J., Carro, M., Chen, Z., Gupta, G.: Constraint answer set programming without grounding and its applications. In: Datalog 2.0 2019–3rd International Workshop on the Resurgence of Datalog in Academia and Industry co-located with the 15th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2019) at the Philadelphia Logic Week 2019, Philadelphia, PA (USA), 4–5 June 2019, pp. 22–26 (2019). http://ceur-ws.org/Vol-2368/paper2.pdf
[2]
Baskar J, Janols R, Guerrero E, Nieves JC, and Lindgren H Montagna S, Abreu PH, Giroux S, and Schumacher MI A multipurpose goal model for personalised digital coaching Agents and Multi-Agent Systems for Health Care 2017 Cham Springer 94-116
[3]
Basu, K., Shakerin, F., Gupta, G.: Aqua: asp-based visual question answering. In: Practical Aspects of Declarative Languages - 22nd International Symposium, PADL 2020, New Orleans, LA, USA, 20–21 January 2020, Proceedings, pp. 57–72 (2020).
[4]
Beck, J.S., Beck, A.T.: Cognitive Therapy: Basics and Beyond. Guilford Press, New York (1995)
[5]
Bell, S., Wood, C., Sarkar, A.: Perceptions of chatbots in therapy. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, p. LBW1712 (2019)
[6]
Bendig, E., Erb, B., Schulze-Thuesing, L., Baumeister, H.: The next generation: chatbots in clinical psychology and psychotherapy to foster mental health-a scoping review. In: Verhaltenstherapie, pp. 1–13 (2019)
[7]
Bliem, B., Kaufmann, B., Schaub, T., Woltran, S.: ASP for anytime dynamic programming on tree decompositions (extended abstract). In: Friedrich, G., Helmert, M., Wotawa, F. (eds.) Proceedings of the 39th German Conference on Artificial Intelligence (KI 2016). LNCS, vol. 9904, pp. 257–263 (2016).
[8]
Brewka, G., Delgrande, J.P., Romero, J., Schaub, T.: asprin: customizing answer set preferences without a headache. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25–30 January 2015, Austin, Texas, USA, pp. 1467–1474 (2015). http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9535
[9]
Brown KW, Ryan RM, and Creswell JD Mindfulness: theoretical foundations and evidence for its salutary effects Psychol. Inq. 2007 18 4 211-237
[10]
Cervantes-Bello, G., De-Los-Santos-Goméz, A., Osorio, M., Andres, M.B.: Artificial intelligence methodology as a tool for the development of psycho-affective learning. In: proceedings of Thirteenth Latin American Workshop on New Methods of Reasoning (LANMR) 2020 in CEUR Workshop Proceedings (CEUR-WS.org). Logic/Languages, Algorithms and New Methods of Reasoning (2009)
[11]
Confalonieri R, Nieves JC, Osorio M, and Vázquez-Salceda J Link S and Prade H Possibilistic semantics for logic programs with ordered disjunction Foundations of Information and Knowledge Systems 2010 Heidelberg Springer 133-152
[12]
Corapi, D., Russo, A., Lupu, E.: Inductive logic programming in answer set programming. In: Inductive Logic Programming - 21st International Conference, ILP 2011, Windsor Great Park, UK, 31 July–3 August 3 2011, Revised Selected Papers, pp. 91–97 (2011).
[13]
Dasgupta, S., Papadimitriou, C.H., Vazirani, U.V.: Algorithms. McGraw-Hill Higher Education New York (2008)
[14]
Daubenmier J, Lin J, Blackburn E, Hecht FM, Kristeller J, Maninger N, Kuwata M, Bacchetti P, Havel PJ, and Epel E Changes in stress, eating, and metabolic factors are related to changes in telomerase activity in a randomized mindfulness intervention pilot study Psychoneuroendocrinology 2012 37 7 917-928
[15]
Davidson, R.J., et al.: Alterations in brain and immune function produced by mindfulness meditation. Psychosom. Med. 65(4), 564–570 (2003). https://www.ncbi.nlm.nih.gov/pubmed/12883106
[16]
Davidson Richard: the four keys to well being. Greater Good Magazine (2016). https://greatergood.berkeley.edu/article/item/the_four_keys_to_well_being
[17]
Delgrande, J., Peppas, P., Woltran, S.: Agm-style belief revision of logic programs under answer set semantics. In: Logic Programming and Nonmonotonic Reasoning, 12th International Conference, LPNMR 2013, Corunna, Spain, 15–19 September 2013. Proceedings, pp. 264–276 (2013)
[18]
Diano, F., Ferrata, F., Calabretta, R.: The development of a mindfulness-based mobile application to learn emotional self-regulation. In: PSYCHOBIT (2019)
[19]
Dovier, A., Formisano, A., Pontelli, E.: Parallel answer set programming. In: Handbook of Parallel Constraint Reasoning, pp. 237–282 (2018).
[20]
Downey RG and Fellows MR Fundamentals of Parameterized Complexity 2013 London Springer
[21]
Dyoub, A., Costantini, S., Lisi, F.A.: Towards ethical machines via logic programming. arXiv preprint arXiv:1909.08255 (2019)
[22]
Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: SEMANTiCS (Posters, Demos, SuCCESS), p. 48 (2016)
[23]
Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: The dlv planning system: progress report. In: Logics in Artificial Intelligence, European Conference, JELIA 2002, Cosenza, Italy, 23–26 September, Proceedings, pp. 541–544 (2002).
[24]
Friedman ES, Koenig AM, and Thase ME Fatemi SH and Clayton PJ Cognitive and Behavioral Therapies The Medical Basis of Psychiatry 2016 New York Springer 781-798
[25]
Gebser M, Grote T, and Schaub T Janhunen T and Niemelä I Coala: a compiler from action languages to ASP Logics in Artificial Intelligence 2010 Heidelberg Springer 360-364
[26]
Gebser M, Kaufmann B, Kaminski R, Ostrowski M, Schaub T, and Schneider M Potassco: the potsdam answer set solving collection AI Commun. 2011 24 2 107-124
[27]
Gelfond, M., Kahl, Y.: Knowledge Representation, Reasoning, and The Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press, New York (2014)
[28]
Gil R, Virgili-Gomá J, García R, and Mason C Emotions ontology for collaborative modelling and learning of emotional responses Comput. Hum. Behav. 2015 51 610-617
[29]
Grasso G, Leone N, Manna M, and Ricca F Balduccini M and Son TC ASP at work: spin-off and applications of the DLV system Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning 2011 Heidelberg Springer 432-451
[30]
Gupta, G.: Next generation logic programming systems: reaching for the holy grail of computer science (2017)
[31]
Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. arXiv preprint arXiv:1709.05584 (2017)
[32]
Hermenegildo, M.V., et al.: An overview of ciao and its design philosophy. TPLP 12(1–2), 219–252 (2012).
[33]
Inkster B, Sarda S, and Subramanian V An empathy-driven, conversational artificial intelligence agent (wysa) for digital mental well-being: real-world data evaluation mixed-methods study JMIR mHealth and uHealth 2018 6 11 e12106
[34]
Jackson, S.: Flow and Mindfulness in Performance. Current Perspectives in Social and Behavioral Sciences, pp. 78–100. Cambridge University Press, New York (2016)
[35]
Jacobs TL et al. Intensive meditation training, immune cell telomerase activity, and psychological mediators Psychoneuroendocrinology 2011 36 5 664-681
[36]
Jayaraman, B., Moon, K.: Subset logic programs and their implementation. J. Log. Program. 42(2), 71–110 (2000).
[37]
Jingar, M., Lindgren, H.: Tangible communication of emotions with a digital companion for managing stress: an exploratory co-design study. In: Proceedings of the 7th International Conference on Human-Agent Interaction, pp. 28–36 (2019)
[38]
Kabat-Zinn, J.: Wherever You Go, There You Are: Mindfulness Meditation in Everyday Life. Hachette Books, Paris (2009)
[39]
Katzouris, N., Artikis, A., Paliouras, G.: Incremental learning of event definitions with inductive logic programming. Mach. Learn. 100(2–3), 555–585 (2015).
[40]
Kendig CE What is proof of concept research and how does it generate epistemic and ethical categories for future scientific practice? Sci. Eng. Ethics 2016 22 3 735-753
[41]
Knapp, P., Beck, A.T.: Cognitive therapy: foundations, conceptual models, applications and research fundamentos, modelos conceituais, aplicações e pesquisa da terapia cognitiva. Rev Bras Psiquiatr. 2008(30 Suppl II), S54–64 (2008). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.611.1005&rep=rep1&type=pdf
[42]
Levitt HM Sounds of silence in psychotherapy: the categorization of clients’ pauses Psychother. Res. 2001 11 3 295-309
[43]
Lieberman, H., Mason, C.: Intelligent agent software for medicine. In: Studies in Health Technology and Informatics, pp. 99–110 (2002)
[44]
Lifschitz, V.: Answer set programming and plan generation. Artif. Intell. 138(1–2), 39–54 (2002).
[45]
Luksha, P., Cubista, J., Laszlo, A., Popovich, M., Ninenko, I.: Educational ecosystems for societal transformation (2017)
[46]
MacKenzie MB, Abbott KA, and Kocovski NL Mindfulness-based cognitive therapy in patients with depression: current perspectives Neuropsychiat. Dis. Treat. 2018 14 1599
[47]
Marple, K., Salazar, E., Gupta, G.: Computing stable models of normal logic programs without grounding. CoRR abs/1709.00501 (2017). http://arxiv.org/abs/1709.00501
[48]
Mason, C.: The multi-disciplinary case for human sciences in technology design. In: AAAI Fall Symposia (2014)
[49]
Mason C Engineering kindness: building a machine with compassionate intelligence Int. J. Synth. Emot. (IJSE) 2015 6 1 1-23
[50]
Mason C and Mason E Haptic medicine Stud. Health Technol. Inf. 2009 149 368-385
[51]
Mason, C.L.: Human-level AI requires compassionate intelligence. In: AAAI Workshop on Meta-Cognition (2008)
[52]
Mazza, N.: Poetry Therapy: Theory and Practice. Routledge, London (2016)
[53]
Morak M, Pichler R, Rümmele S, and Woltran S Janhunen T and Niemelä I A dynamic-programming based ASP-solver Logics in Artificial Intelligence 2010 Heidelberg Springer 369-372
[54]
Muggleton SH, Tamaddoni-Nezhad A, and Lisi FA Inductive Logic Programming 2012 Heidelberg Springer
[55]
Nakamura J and Csikszentmihalyi M The Concept of Flow Flow and the Foundations of Positive Psychology 2014 Dordrecht Springer 239-263
[56]
Nieves, J.C., Cortés, U., Osorio, M., Centia, S., Mártir, C.: Supporting decision making in organ transplanting using argumentation theory. In: LA-NMR (2006)
[57]
Nieves, J.C., Cortés, U., Osorio, M., Olmos, I., Gonzalez, J.A.: Defining new argumentation-based semantics by minimal models. In: Seventh Mexican International Conference on Computer Science, ENC 2006, 18–22 September 2006, San Luis Potosi, Mexico. pp. 210–220. IEEE Computer Society (2006).
[58]
Nieves, J.C., Osorio, M., Zepeda, C.: A schema for generating relevant logic programming semantics and its applications in argumentation theory. Fundam. Informaticae 106(2–4), 295–319 (2011).
[59]
Okoshi, T., Nakazawa, J., Ko, J.G., Kawsar, F., Pirttikangas, S.: Wellcomp 2019: second international workshop on computing for well-being. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 1146–1149 (2019)
[60]
Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, New York (1990)
[61]
Osorio, M., Zepeda, C., Carballido, J.L.: Myubot: towards an artificial intelligence agent system chat-bot for well-being and mental health. (2020)
[62]
Osorio, M., Zepeda, C., Carballido, J.L.: Towards a virtual companion system to give support during confinement. In: CONTIE (2020)
[63]
Osorio, M., Zepeda, C., Castillo, H., Cervantes, P., Carballido, J.L.: My university e-partner. In: CONTIE, pp. 150–1503 (2019)
[64]
Osorio, M., Corona, E.: The a-pol system. In: Answer Set Programming, Advances in Theory and Implementation, Proceedings of the 2nd International ASP 2003 Workshop, Messina, Italy, 26–28 September 2003 (2003). http://ceur-ws.org/Vol-78/asp03-final-osorio-apol.pdf
[65]
Osorio, M., Cuevas, V.: Updates in answer set programming: an approach based on basic structural properties. CoRR abs/cs/0609167 (2006)
[66]
Osorio, M., Jayaraman, B.: Aggregation and negation-as-failure. New Gener. Comput. 17(3), 255–284 (1999).
[67]
Osorio M, Jayaraman B, and Plaisted DA Theory of partial-order programming Sci. Comput. Program. 1999 34 3 207-238
[68]
Osorio M and Nieves JC Gelbukh A and Kuri Morales ÁF PStable semantics for possibilistic logic programs MICAI 2007: Advances in Artificial Intelligence 2007 Heidelberg Springer 294-304
[69]
Osorio, M., Nieves, J.C., Jayaraman, B.: Aggregation in functional query languages. J. Funct. Logic Program. 2004 (2004). http://danae.uni-muenster.de/lehre/kuchen/JFLP/articles/2004/A2004-02/A2004-02.html
[70]
Osorio, M.J., Montiel-Moreno, L.A., Rojas-Velázquez, D., Nieves, J.C.: E-friend: a logical-based ai agent system chat-bot for emotional well-being and mental health. In: To be published in Communications in Computer and Information Science CCIS. Accepted in 1st International Workshop on Deceptive AI at ECAI2020, 30 August 2020. Santiago de Compostela, Spain (2020)
[71]
Palanica A, Flaschner P, Thommandram A, Li M, and Fossat Y Physicians’ perceptions of chatbots in health care: Cross-sectional web-based survey J. Med. Internet Res. 2019 21 4 e12887
[72]
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics (2004)
[73]
Pendharkar, D., Gupta, G.: An ASP based approach to answering questions for natural language text. In: Practical Aspects of Declarative Languages - 21th International Symposium, PADL 2019, Lisbon, Portugal, 14–15 January 2019, Proceedings, pp. 46–63 (2019).
[74]
Perri, S., Ricca, F., Sirianni, M.: Parallel instantiation of ASP programs: techniques and experiments. TPLP 13(2), 253–278 (2013).
[75]
Ribeiro IJ, Pereira R, Freire IV, de Oliveira BG, Casotti CA, and Boery EN Stress and quality of life among university students: a systematic literature review Health Prof. Educ. 2018 4 2 70-77
[76]
Robinson, K., Aronica, L.: Finding Your Element: How to Discover Your Talents and Passions and Transform Your Life. Penguin Books, London (2014)
[77]
Rodríguez, R.S., Roldán, C.Y.C., Eisele, J.G., del Pilar Gómez Gil, M., Galindo, M.J.O.: Algorithms for the typing of related DNA sequences. In: 15th International Conference on Electronics, Communications, and Computers (CONIELECOMP 2005), 28 February 2005–2 March 2005, Puebla, Mexico, pp. 268–271 (2005).
[78]
Rosenkranz MA, Dunne JD, and Davidson RJ The next generation of mindfulness-based intervention research: what have we learned and where are we headed? Curr. Opin. Psychol. 2019 28 179
[79]
Rotmensch M, Halpern Y, Tlimat A, Horng S, and Sontag D Learning a health knowledge graph from electronic medical records Sci. Rep. 2017 7 1 1-11
[80]
Sarkadi Ş, Panisson AR, Bordini RH, McBurney P, Parsons S, and Chapman M Modelling deception using theory of mind in multi-agent systems AI Commun. 2019 32 4 287-302
[81]
Sarlej, M.: A lesson learned: using emotions to generate stories with morals. Ph.D. thesis, Doctoral dissertation). Computer Science and Engineering, University of New 026 (2014)
[82]
Schaub T and Woltran S Answer set programming unleashed! KI-Künstliche Intelligenz 2018 32 2–3 105-108
[83]
Schwarze MJ and Gerler ER Jr Using mindfulness-based cognitive therapy in individual counseling to reduce stress and increase mindfulness: an exploratory study with nursing students Prof. Counselor 2015 5 1 39
[84]
Scraper, R.L.: The art and science of maieutic questioning within the socratic method. In: International Forum for Logotherapy. Viktor Frankl Inst of Logotherapy (2000)
[85]
Stanford Medicine: the center for compassion and altruism research and education. CCARE (2020). http://ccare.stanford.edu/
[86]
Steunebrink, B.R., Dastani, M., Meyer, J.J.C.: The OCC model revisited. In: Proceedings of the 4th Workshop on Emotion and Computing. Association for the Advancement of Artificial Intelligence (2009)
[87]
Voloch N Finding the most efficient paths between two vertices in a knapsack-item weighted graph Int. J. Adv. Comput. Res. 2017 7 28 15
[88]
Walton D The place of dialogue theory in logic, computer science and communication studies Synthese 2000 123 3 327-346
[89]
Woodward, K., et al.: Beyond mobile apps: a survey of technologies for mental well-being. IEEE Trans. Affect. Comput. (2020)
[90]
World Health Organization: World health organization. WHO (2020). https://www.who.int/
[91]
Xiang, X., Wang, Z., Jia, Y., Fang, B.: Knowledge graph-based clinical decision support system reasoning: a survey. In: 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC), pp. 373–380. IEEE (2019)
[92]
Zhuang, Z., Delgrande, J.P., Nayak, A.C., Sattar, A.: A new approach for revising logic programs. CoRR abs/1603.09465 (2016)
Index Terms
- Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution: Applied in Cecilia: An Architecture of a Digital Companion Artificial Intelligence (AI) Agent System Composer of Dialogue Scripts for Well-Being and Mental Health
Index terms have been assigned to the content through auto-classification.
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DOI:10.1007/978-3-030-72308-8
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