Generating Qualitative Causal Graph using Modeling Constructs of Qualitative Process Theory for Explaining Organic Chemistry Reactions
This paper discusses the causal explanation capability
of QRIOM, a tool aimed at supporting learning of organic chemistry
reactions. The development of the tool is based on the hybrid use of
Qualitative Reasoning (QR) technique and Qualitative Process
Theory (QPT) ontology. Our simulation combines symbolic,
qualitative description of relations with quantity analysis to generate
causal graphs. The pedagogy embedded in the simulator is to both
simulate and explain organic reactions. Qualitative reasoning through
a causal chain will be presented to explain the overall changes made
on the substrate; from initial substrate until the production of final
outputs. Several uses of the QPT modeling constructs in supporting
behavioral and causal explanation during run-time will also be
demonstrated. Explaining organic reactions through causal graph
trace can help improve the reasoning ability of learners in that their
conceptual understanding of the subject is nurtured.
[1] K. Forbus, and D. Gentner, "Qualitative mental models: Simulations or
memories?" Proc. of the 11th International workshop on qualitative
reasoning, Cortona, Italy, 1997.
[2] I. Iwasaki, "Real-world applications of qualitative reasoning", IEEE
Expert, 1997, pp. 16-21.
[3] K. Forbus, P. Whalley, J. Everett, L. Ureel, M. Brokowski, J. Baher, and
S. Kuehne, "CyclePad: an articulate virtual laboratory for engineering
thermodynamics," Artificial Intelligence Journal, Vol. 114 , 1999, pp.
297-347.
[4] A. Bouwer, and B. Bredeweg, "VisiGarp: graphical representation of
qualitative simulation models," in: J.D. Moore, G. Luckhardt Redfield,
J.L. Johnson (Eds.), Artificial Intelligence in Education: AI-ED in the
Wired and Wireless Future, Amsterdam, The Netherlands, 2001, pp.
294-305.
[5] S.M.F.D. Syed Mustapha, J.S. Pang, and S.M. Zain, "Application of
Qualitative Process Theory to Qualitative Simulation and Analysis of
Inorganic Chemical Reaction", Proc. of the 16th International Workshop
of Qualitative Reasoning, Barcelona, Spain, 2002.
[6] S.M.F.D. Syed Mustapha, J.S. Pang, and S.M. Zain, "QALSIC: towards
building an articulate educational software using qualitative process
theory approach in inorganic chemistry for high school level,"
International Journal of Artificial Intelligence in Education, 15 (3):
229-257, 2005.
[7] G. Biswas, D. Schwartz, J. Bransford, and The Teachable Agents Group
at Vanderbilt, "Technology Support for Complex Problem Solving:
From SAD Environment to AI", in K. Forbus, and P. Feltovich, (Eds.),
Smart Machines in Education, Menlo Park Calif.: AAAI Press, pp. 71-
97, 2001.
[8] I. Bratko, and D. Suc, "Qualitative explanation of controllers", Proc. of
the 16th International Workshop on Qualitative Reasoning, June 10-12,
Barcelona, Spain, 2002, pp. 1-2.
[9] I. Bratko, and D. Suc, "Learning Qualitative Models", AI Magazine
24(4): 107-119, 2003.
[10] LHASA research group home page. Available: http://derek.harvard.edu/.
[11] D. P. Dolata, "Artificial intelligence in Chemistry", Ohio University,
Athens, OH, USA, 1993.
[12] K. Forbus, "Qualitative process theory," Artificial Intelligence, Vol. 24,
1984, pp. 85-168.
[13] Y.C. Alicia Tang, and S.M.F.D. Syed Mustapha, "Representing SN1
reaction mechanism using the qualitative process theory," in: C. Bailey-
Kellogg, B. Kuipers, (Eds.), Proc. of the 20th International Workshop on
Qualitative Reasoning, Hanover, USA, 2006, pp. 137-147.
[14] K. Forbus, "Articulate software for science and engineering education",
in K. Forbus, P. Feltovich, and A. Canas (Eds), Smart machines in
education: the coming revolution in educational technology, AAAI
Press, 2001.
[15] D. Gentner, and A. Stevens, "Mental models", IEA Associates, 1983.
[16] B. Bredeweg, and K. Forbus, "Qualitative modeling in education," AI
Magazine, 24(4): 35-46, 2003.
[17] D.E. Penner, "Cognition, Computers and Synthetic Science: Building
knowledge and meaning through modeling", Review of research in
education, 25: 1-36, 2001.
[18] Amzi! Prolog Home Page. Available: http://www.amzi.com
[1] K. Forbus, and D. Gentner, "Qualitative mental models: Simulations or
memories?" Proc. of the 11th International workshop on qualitative
reasoning, Cortona, Italy, 1997.
[2] I. Iwasaki, "Real-world applications of qualitative reasoning", IEEE
Expert, 1997, pp. 16-21.
[3] K. Forbus, P. Whalley, J. Everett, L. Ureel, M. Brokowski, J. Baher, and
S. Kuehne, "CyclePad: an articulate virtual laboratory for engineering
thermodynamics," Artificial Intelligence Journal, Vol. 114 , 1999, pp.
297-347.
[4] A. Bouwer, and B. Bredeweg, "VisiGarp: graphical representation of
qualitative simulation models," in: J.D. Moore, G. Luckhardt Redfield,
J.L. Johnson (Eds.), Artificial Intelligence in Education: AI-ED in the
Wired and Wireless Future, Amsterdam, The Netherlands, 2001, pp.
294-305.
[5] S.M.F.D. Syed Mustapha, J.S. Pang, and S.M. Zain, "Application of
Qualitative Process Theory to Qualitative Simulation and Analysis of
Inorganic Chemical Reaction", Proc. of the 16th International Workshop
of Qualitative Reasoning, Barcelona, Spain, 2002.
[6] S.M.F.D. Syed Mustapha, J.S. Pang, and S.M. Zain, "QALSIC: towards
building an articulate educational software using qualitative process
theory approach in inorganic chemistry for high school level,"
International Journal of Artificial Intelligence in Education, 15 (3):
229-257, 2005.
[7] G. Biswas, D. Schwartz, J. Bransford, and The Teachable Agents Group
at Vanderbilt, "Technology Support for Complex Problem Solving:
From SAD Environment to AI", in K. Forbus, and P. Feltovich, (Eds.),
Smart Machines in Education, Menlo Park Calif.: AAAI Press, pp. 71-
97, 2001.
[8] I. Bratko, and D. Suc, "Qualitative explanation of controllers", Proc. of
the 16th International Workshop on Qualitative Reasoning, June 10-12,
Barcelona, Spain, 2002, pp. 1-2.
[9] I. Bratko, and D. Suc, "Learning Qualitative Models", AI Magazine
24(4): 107-119, 2003.
[10] LHASA research group home page. Available: http://derek.harvard.edu/.
[11] D. P. Dolata, "Artificial intelligence in Chemistry", Ohio University,
Athens, OH, USA, 1993.
[12] K. Forbus, "Qualitative process theory," Artificial Intelligence, Vol. 24,
1984, pp. 85-168.
[13] Y.C. Alicia Tang, and S.M.F.D. Syed Mustapha, "Representing SN1
reaction mechanism using the qualitative process theory," in: C. Bailey-
Kellogg, B. Kuipers, (Eds.), Proc. of the 20th International Workshop on
Qualitative Reasoning, Hanover, USA, 2006, pp. 137-147.
[14] K. Forbus, "Articulate software for science and engineering education",
in K. Forbus, P. Feltovich, and A. Canas (Eds), Smart machines in
education: the coming revolution in educational technology, AAAI
Press, 2001.
[15] D. Gentner, and A. Stevens, "Mental models", IEA Associates, 1983.
[16] B. Bredeweg, and K. Forbus, "Qualitative modeling in education," AI
Magazine, 24(4): 35-46, 2003.
[17] D.E. Penner, "Cognition, Computers and Synthetic Science: Building
knowledge and meaning through modeling", Review of research in
education, 25: 1-36, 2001.
[18] Amzi! Prolog Home Page. Available: http://www.amzi.com
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:60772", author = "Alicia Y. C. Tang and Rukaini Abdullah and Sharifuddin M. Zain and Noorsaadah A. Rahman", title = "Generating Qualitative Causal Graph using Modeling Constructs of Qualitative Process Theory for Explaining Organic Chemistry Reactions", abstract = "This paper discusses the causal explanation capability
of QRIOM, a tool aimed at supporting learning of organic chemistry
reactions. The development of the tool is based on the hybrid use of
Qualitative Reasoning (QR) technique and Qualitative Process
Theory (QPT) ontology. Our simulation combines symbolic,
qualitative description of relations with quantity analysis to generate
causal graphs. The pedagogy embedded in the simulator is to both
simulate and explain organic reactions. Qualitative reasoning through
a causal chain will be presented to explain the overall changes made
on the substrate; from initial substrate until the production of final
outputs. Several uses of the QPT modeling constructs in supporting
behavioral and causal explanation during run-time will also be
demonstrated. Explaining organic reactions through causal graph
trace can help improve the reasoning ability of learners in that their
conceptual understanding of the subject is nurtured.", keywords = "Qualitative reasoning, causal graph, organicreactions, explanation, QPT, modeling constructs.", volume = "3", number = "11", pages = "628-11", }
