, H.; and Wallach, D. 1999. Cognitive modeling in perspective. Kognitionswissenschaft . 8(1): 1-4. Stewart, T.C. 2007. A Methodology for Computational Cognitive Modelling . Ph.D. Dissertation, Institute of Cognitive Science, Carleton University, Ottawa, Ontario. Stewart, T.C.; and West, R. 2007. Equivalence: A novel basis for model comparison. In Proceedings of the Twenty-Ninth Conference of the Cognitive Science Society . Mahwah, NJ: Erlbaum. Stewart, T.C.; West, R.; and Lebiere, C
Terrence Stewart and Robert West
Lyudmila Onokoy and Jurijs Lavendels
The article investigates different approaches to the design of information systems. Much attention is paid to comparative analysis of criteria for selecting methodologies for software development, and also to not well-known methodology of DevOps (Development & Operation) , , which aims at consolidation of software developers (Development) and IT professionals’ (Operation) efforts, and automation of implementation process. In conclusion, based on the retrospective analysis and practical experience, the authors formulate regularities and prospects of information systems design methodology development.
Frank van der Velde
The ability to learn constructions may be important for the development of a self-organizing architecture for artificial general intelligence. Constructions are structural relations between more specific or more abstract conceptual representations. They can be derived from the processes of alignment, collocations and distributed equivalences. An architecture that integrates in situ grounded representations with cognitive productivity is ideally suited to learn constructions. This paper described such an architecture, based on neuronal assembly structures and neuronal ’blackboards’ for grounded compositional representations. The paper outlines how constructions could be learned in such an architecture and how the architecture could eventually develop into an autonomous self-organizing architecture for artificial general intelligence.
Peter Eckersley and Anders Sandberg
Secret of Human Thought Revealed, Viking Adult. Levy, J. S.; Thompson, W. R. 2009. Causes of War. John Wiley & Sons. Mockus, A.; Fielding, R. T.; Hebsleb, J. D. 2002. Two case studies of open source software development: Apache and Mozilla. ACM Transactions on Software Engineering and Methodology. 11(3):309-346. Muehlhauser, L.; Salamon, A. 2012. Intelligence Explosion: Evidence and Import. In Singularity Hypotheses: A Scientific and Philosophical Assessment, ed. A. Eden, J. Søraker, J. H. Moor, and E. Steinhart. Berlin
Kevin Gluck, Clayton Stanley, L. Moore, David Reitter and Marc Halbrügge
Exploration for Understanding in Cognitive Modeling
The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments - may achieve an improved understanding of architectures and models - if there is more emphasis on systematic sensitivity and necessity analyses during model development, evaluation, and comparison. We demonstrate this methodological prescription with two of the models submitted for the Dynamic Stocks and Flows (DSF) Model Comparison Challenge, exploring the complex interactions among architectural mechanisms, knowledge-level strategy variants, and task conditions. To cope with the computational demands of these analyses we use a predictive analytics approach similar to regression trees, combined with parallelization on high performance computing clusters, to enable large scale, simultaneous search and exploration.
Christian Lebiere, Cleotilde Gonzalez and Walter Warwick
Editorial: Cognitive Architectures, Model Comparison and AGI
Cognitive Science and Artificial Intelligence share compatible goals of understanding and possibly generating broadly intelligent behavior. In order to determine if progress is made, it is essential to be able to evaluate the behavior of complex computational models, especially those built on general cognitive architectures, and compare it to benchmarks of intelligent behavior such as human performance. Significant methodological challenges arise, however, when trying to extend approaches used to compare model and human performance from tightly controlled laboratory tasks to complex tasks involving more open-ended behavior. This paper describes a model comparison challenge built around a dynamic control task, the Dynamic Stocks and Flows. We present and discuss distinct approaches to evaluating performance and comparing models. Lessons drawn from this challenge are discussed in light of the challenge of using cognitive architectures to achieve Artificial General Intelligence.
Peter C. R. Lane and Fernand Gobet
, L.; and Goldberg, R., eds., Evolutionary Multi-Objective Optimization. London, UK: Springer-Verlag. 7-32. Cooper, R. P., and Shallice, T. 1995. Soar and the case for unified theories of cognition. Cognition 55:115-49. Cooper, R. P.; Fox, J.; Farringdon, J.; and Shallice, T. 1996. A systematic methodology for cognitive modelling. Artificial Intelligence 85:3-44. Cooper, R. P. 2002. Modelling high-level cognitive processes. Mahwah, NJ: Erlbaum. Feigenbaum, E. A., and Simon, H. A. 1984. EPAM-like models
Petros Stefaneas and Ioannis M. Vandoulakis
Halpin and Alexandre Monnin. Volume 43, Issue 4, pp. 480–498. Reprinted in: Harry Halpin and Alexandre Monnin (Eds) Philosophical Engineering: Toward a Philosophy of the Web . Wiley-Blackwell, 2014, pp. 149-167. Stefaneas, Petros and Vandoulakis, Ioannis. 2014. Proofs as spatio-temporal processes”, Pierre Edouard Bour, Gerhard Heinzmann, Wilfrid Hodges and Peter Schroeder-Heister (Eds) “Selected Contributed Papers from the 14 th International Congress of Logic, Methodology and Philosophy of Science”, Philosophia Scientiæ , 18 (3), pp. 111-125. Stefaneas
: Theory and application to reward shaping. In ICML, volume 99, 278-287. Ogata, T., and Sugano, S. 1997. Emergence of Robot Behavior Based on Self-Preservation. Research Methodology and Embodiment of Mechanical System. Journal of the Robotics Society of Japan 15(5):710-721. Omohundro, Stephen M, S. M. 2008. The Basic AI Drives. In Artificial General Intelligence, 2008: Proceedings of the First AGI Conference, volume 171, 483. IOS Press. Pfeifer, R., and Scheier, C. 1999. Understanding intelligence. MIT press
Kristinn Thórisson and Helgi Helgasson
. Constructionist Design Methodology for Interactive Intelligences. AI Magazine . 25(4): 77-90. Thórisson, K. R. 2009. From Constructionist to Constructivist A. I. Keynote, Technical Report, FS-09-01, AAAI press, Menlo Park, Calif. Wang, P. 1995. Non-Axiomatic Reasoning System: Exploring the Essence of Intelligence . Ph.D. diss., Dept. of Computer Science, Indiana Univ., CITY, Indiana. Wang, P. 1996. Problem-solving under insufficient resources. In Working Notes of the Symposium on Flexible