“Analyzing the Roles of Problem Solving and Learning in Organizational-Learning Oriented Classifier System,” Takadama, K., Nakasuka, S., and Terano, T., (1998) In PRICAI’98: Topics in Artificial Intelligence, New York: Springer.
Conference Paper
Takadama, Nakasuka, and Terano (1998) develop an integrated construct and architechture for organizational problem solving defined as a learning oriented classifier system (pp. 71-82). From which, experiments were done to test the effectiveness of their model. Four “mechanisms” for an integrated model of organizational problem solving and learning are utilized, including reinforcement learning, evolutionary computation, distributed artificial intelligence, and multi-agent environments. Where research generally studies these models independent of one another, integrating the mechanisms is described as classifier system composed of rule generation, exchange, and then the utilization of organizational knowledge. Following, the model provides for a five-part evaluation mechanism within the architecture of the organizational learning oriented classifier system. Within this architecture, the authors maintain agents adapt to dynamic environments through the four mechanisms, where they “obtain [only] their partial environmental states, but they cannot obtain the total environmental states,” in such a way that the “distributed recognition indicates that agents must determine their own behaviors according to their partial states” (Takadama, Nakasuka, and Terano, 1998, p. 72). After defining the learning architecture, the methodology and results of problem-based design experiments are detailed to assess the level of organizational learning.
In 15 experiments, the behaviors of problem solving are measured as “all agents continue to solve the same PCB [printed circuit board] re-design problem dozens of times, and aim to acquire their own function as a sequence of behavior which find appropriate placement” (Takadama, Nakasuka, and Terano, 1998, p. 78). The results suggested that reinforcement learning minimizes problem-solving time but increased the steps, rule generation enabled problem solving, rule exchange reduced the steps in problem solving, and, with the integration of the model, organizational knowledge reduced the steps in problem solving. Takadama, Nakasuka, and Terano (1998) then discuss the findings, maintaining that through rule exchange in problem solving by generating organizational knowledge and from the division of work in multi-agent environments, the four mechanisms “respectively work as an individual single-/double-loop learning and an organizational single-/double-loop learning in organization and management sciences” (pp. 78-82). The authors thus argue the validity of their integrated construct for organizational artificial intelligence experiments to support the learning architecture for problem solving, and then define directions for further research.
Reflection
This paper for the 5th Pacific Rim International Conference on Artificial Intelligence is not exactly a favorite article per se, but it is an important article in my research on artificial intelligence. There are basically two significant aspects of the article: first the underlying assumptions from behavioral psychology, and then the incorporation of those behavioral assumptions with artificial intelligence and organizational learning. The underlying assumptions that relate the perspective to behavioral psychology are clearly evidenced with learning defined as reinforced behaviors. This is to say, as defined by the authors, computational artificial intelligence is not a cognitive process (though of course, there are cognitive perspectives in artificial intelligence, which could be defined as “constructivist” theories). As the article is poorly written, to summarize one might think of the experiment as giving a group of individuals a rubiks cube to solve. There is a one best way to solve the cube, and the authors maintain that their model of organizational learning support an explanation of how through group problem solving the cube can be best solved. As noted, experimenters do not see the process of problem solving as a cognitive process of learning. Instead, solving the cube is a process of reinforcing behaviors through organizational knowledge and rule exchange (i.e., programmed group learning - Skinnerian rats solving a circut board maze).
The difference between the two as means to the end is complicated and gets real chin scratching, but is important in my opinion. The behavioral perspective exemplifies my many reservations over trends in learning theory with technological change and online environmentss. And, I have found articles like it that more specifically apply such concepts to learning online and engineering curriculum. The integrated model defined in the article does provide an interesting frame of reference on deductive processes in organizational learning by developing the problem solving construct as a sort of computational social construction of organizational knowledge, which addresses relevant points for discussion with the behaviorist and constructivist dichotomy. One point misunderstood is that both theories do recognize deductive knowledge exists independent of the mind’s experience; however, in addition to their differentiated views on the role of cognition and learning behavior, the theories have very different understandings concerning the a priority of that knowledge. In terms of Kantian philosophy, the difference relates to Kant's antimony and whether the object conforms to the mind or the mind to the object. In answering these questions, contemporary constructivists and the evolution of Kantian philosophy does not fail to recognize the architecture of the mind; the most frequent criticism of behavioral is that it fails to account for the way in which the mind actively learns. The same again, the authors define learning as noncognitive reinforcing and controlling behaviors to achieve desired outcomes. The paper situates these themes in a context for organizational psychology, complicating the discussion with the contemporary topics artificial intelligence and computational theory.
Anyway, seminal theorists in organizational learning would very likely share my reservations over the behavioral perspective and the integration of artificial intelligence with theories of organizational learning. Literatures could be cited warning of such developments. The authors should certainly be critiqued for a very surface level discussion and application of learning theories. Historical research would also support the potential consequences of technocratic behariorism. When it boils down to it in my opinion, debates over computational views of the mind and artificial intelligence are in some ways simply a philosophical exercise. Really, I prefer not think of learning theories and psychology in terms of computation and artificial intelligence as they reflect the industrial age metaphors of the mind that are subject to misinterpretation. However, they are simply metaphors, and it is the underlying assumptions that are important to understand where theories diverge between perspectives that are commonly defined as behavioral and cognitive psychologies. While the dichotomy is commonly misunderstood, integrating them requires in depth knowledge of the theories for an effective synthesis and application; including from a political frame in understanding underlying assumptions and the differences from theory into practice. Many of these issues have been on my mind lately, so I decided to share some of my views on the paper.
