High-level Behavior Representation Languages Revisited

Abstract : There has only been a short history of high level languages to model human cognition based on cognitive architectures. TAQL is an early example (Yost, 1993). TAQL showed a large (3x) speed increase over plain Soar, but because it did not support Soar's learning mechanism and because Soar changed soon after its release, TAQL's impact was not as great as its developer probably would have liked. It is time again to consider high level behavior representation languages. Cognitive models and intelligent agents are becoming more complex and pervasive. This is driving the need for development environments that make it easier to create, share, and reuse cognitive models. Several high level modeling languages have recently been created and several of them are described briefly here. These languages are each different, but they have a common goal of making modeling human data easier to perform. We can now see some generalities and common lessons. By holding this symposium we will identify lessons for the development of these languages as well as for their users. These languages are reviewed briefly in the next section.

[1]  Richard L. Lewis,et al.  A Constraint-Based Approach to Understanding the Composition of Skill , 2004, ICCM.

[2]  Frank E. Ritter,et al.  Explaining Soar: Analysis of Existing Tools and User Information Requirements , 2003 .

[3]  Kenneth R. Koedinger,et al.  Predictive human performance modeling made easy , 2004, CHI.

[4]  Frank E. Ritter,et al.  Herbal: A high-level language and development environment for developing cognitive models in Soar , 2005 .

[5]  G.P. Morgan,et al.  Increasing efficiency of the development of user models , 2005, 2005 IEEE Design Symposium, Systems and Information Engineering.

[6]  Frank J. Lee,et al.  Simple cognitive modeling in a complex cognitive architecture , 2003, CHI '03.

[7]  Richard L. Lewis,et al.  Information-Requirements Grammar: A Theory of the Structure of Competence for Interaction , 2005 .

[8]  Richard L. Lewis,et al.  A constraint satisfaction approach to predicting skilled interactive performance , 2004, CHI 2004.

[9]  Frank E. Ritter,et al.  Specifying ACT-R models of user interaction with a GOMS language , 2005, Cognitive Systems Research.

[10]  Allen Newell,et al.  The keystroke-level model for user performance time with interactive systems , 1980, CACM.

[11]  Gregg R. Yost,et al.  Acquiring knowledge in Soar , 1993, IEEE Expert.

[12]  Richard P. Cooper,et al.  A Systematic Methodology for Cognitive Modelling , 1996, Artif. Intell..

[13]  Richard L. Lewis,et al.  Generating automated predictions of behavior strategically adapted to specific performance objectives , 2006, CHI.

[14]  Frank E. Ritter,et al.  A Tutorial on Herbal: A High-Level Language and Development Environment Based on Protégé for Developing Cognitive Models in Soar , 2005 .

[15]  Leon Urbas,et al.  Model Based Analysis and Design of Human-Machine Dialogues through Displays , 2005, Künstliche Intell..

[16]  Randolph M. Jones,et al.  An Abstract Language for Cognitive Modeling , 2005 .