Formalization and Analysis of Reasoning by Assumption

This article introduces a novel approach for the analysis of the dynamics of reasoning processes and explores its applicability for the reasoning pattern called reasoning by assumption. More specifically, for a case study in the domain of a Master Mind game, it is shown how empirical human reasoning traces can be formalized and automatically analyzed against dynamic properties they fulfill. To this end, for the pattern of reasoning by assumption a variety of dynamic properties have been specified, some of which are considered characteristic for the reasoning pattern, whereas some other properties can be used to discriminate among different approaches to the reasoning. These properties have been automatically checked for the traces acquired in experiments undertaken. The approach turned out to be beneficial from two perspectives. First, checking characteristic properties contributes to the empirical validation of a theory on reasoning by assumption. Second, checking discriminating properties allows the analyst to identify different classes of human reasoners.

[1]  David Klahr,et al.  Dual Space Search During Scientific Reasoning , 1988, Cogn. Sci..

[2]  Jan Treur,et al.  Formal Specification of Complex Reasoning Systems , 1993 .

[3]  Tibor Bosse,et al.  Modelling Shared Extended Mind and Collective Representational Content , 2004, SGAI Conf..

[4]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[5]  S. Bringsjord,et al.  Mental MetaLogic: a New Paradigm in Psychology of Reasoning , 2001 .

[6]  Herbert A. Simon,et al.  Problem solving and rule induction: A unified view. , 1974 .

[7]  D. Knuth The Computer as Master Mind , 1977 .

[8]  Dieter Fensel,et al.  A comparison of languages which operationalize and formalize KADS models of expertise , 1994, The Knowledge Engineering Review.

[9]  Catholijn M. Jonker,et al.  Analysis of the Dynamics of Reasoning Using Multiple Representations , 2002 .

[10]  Catholijn M. Jonker,et al.  Compositional Verification of Multi-Agent Systems: A Formal Analysis of Pro-activeness and Reactiveness , 1997, Int. J. Cooperative Inf. Syst..

[11]  G. Miller,et al.  Cognitive science. , 1981, Science.

[12]  Jan Treur,et al.  Temporal Semantics of Compositional Task Models and Problem Solving Methods , 1999, Data Knowl. Eng..

[13]  Alex M. Andrew,et al.  Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems , 2002 .

[14]  Catholijn M. Jonker,et al.  Modelling the dynamics of reasoning processes: Reasoning by assumption , 2003, Cognitive Systems Research.

[15]  Rocky Ross,et al.  Mental models , 2004, SIGA.

[16]  Keith Stenning,et al.  A working memory model of relations between interpretation and reasoning , 2005 .

[17]  K. Koyama,et al.  An Optimal Mastermind Strategy , 1993 .

[18]  Eric Dubois,et al.  Supporting the analyst when reasoning on requirements specifications for real-time and distributed systems , 1998, Proceedings First International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC '98).

[19]  Daniela E. Damian,et al.  Specification of Bahavioural Requirements within Compositional Multi-agent System Design , 1999, MAAMAW.

[20]  L. Rips The Psychology of Proof: Deductive Reasoning in Human Thinking , 1994 .

[21]  Tibor Bosse,et al.  SIMULATION AND ANALYSIS OF CONTROLLED MULTI-REPRESENTATIONAL REASONING PROCESSES , 2007, Appl. Artif. Intell..

[22]  R. Byrne Suppressing valid inferences with conditionals , 1989, Cognition.

[23]  Stephen Fickas,et al.  Goal-Directed Requirements Acquisition , 1993, Sci. Comput. Program..