Modeling Multi-Agent Chaos: Killing Aliens and Managing Difficult People

Modeling Multi-Agent Chaos: Killing Aliens and Managing Difficult People W. Korey MacDougall (warrenmacdougall@carleton.ca) Robert L. West (robert_west@carleton.ca) Emmanuelle Hancock (emme.hancock@gmail.com) Institute of Cognitive Science, Carleton University, Ottawa, Ontario, Canada Abstract Understanding macro cognition is important for understanding how experts and lay people function in the real world, and for building safer, more effective socio-technical systems. We present a framework and a methodology for creating and evaluating process models of highly dynamic expert tasks and illustrate it with two models. Keywords: Macro Cognition; Cognitive Modeling; Cognitive Architectures; Expertise; Videogame playing; Mediation; Introduction Collectively we have developed innumerable forms of expertise, differing wildly in their appearances. These range from trapping animals to space flight, poetry to plumbing. Beneath the extreme diversity of forms, however, there may be consistency in the structure and acquisition of expertise. It may be that expertise (and our capacity to develop it) is rather simple and straightforward, but that its manifestations are so varied because it emerges in many vastly different domains. This is akin to the point made by Herbert Simon’s famous “ant on the beach” metaphor (Simon, 1969), in which he argues that the apparent complexity of an ant’s behavior as it moves in a convoluted path across a beach is largely attributable to the complexity of the environment, and not to any sophisticated scheming or strategizing by the ant. We think it is worth investigating whether the situation is similar regarding human expertise, but feel that the techniques and concepts which would permit such a study are in need of further refinement. We offer here a prospective research methodology that we are developing to address this using the SGOMS macro architecture (West & Pronovost, 2009; West et al., 2013), and describe two models which we have constructed to test the method. The first model is of people playing a video game, “Gears of War 3” (produced by Epic Games) and the second is of professional mediators. Studying Experts Expertise is a topic of theoretical and practical importance in a number of fields, including psychology, sociology, artificial intelligence, and education. Its relevance to multiple communities has led to the development of varied conceptual frameworks that distinguish experts from non- experts according to mental capacities, experience over time, representation and organization of knowledge, elite achievement, status within a community, or reliably superior performance (Ericsson, 2006). From these conceptual frameworks have developed a number of methods for studying experts, including, inter alia, laboratory based methods (Chi, 2006), naturalistic observation (Ball & Ormerod, 2000), the engineering of expert systems (Fink & Lush, 1987), and simulation of expert behavior and cognition (West & Somers, 2011). Existing conceptions of expertise, and their associated research methodologies, have provided important insights into many aspects of expert performance, such as differences in knowledge representations between experts and novices (French et al., 1996), the psychological traits most frequently associated with the development of expertise (Shanteau, 1998), the importance of “deliberate practice” in acquiring mastery, (Ericsson, 2006), and the role of social factors and institutions in facilitating the development of expertise (Hunt, 2006). Additionally, AI- based research into expertise has led to the creation of a number of expert systems capable of supplementing or replacing the performance of human experts. Examples of these include systems for medical diagnosis (Saito & Nakano, 1988), hypothesis formation (Buchanan, Sutherland, & Feigenbaum, 1968), and chess analysis (Michie, 1972). There are, however, significant aspects of expertise which are not as amenable to investigation using existing frameworks. Specifically, these methods do not lead to a process model. That is, a model that, given the current state of the agent, the task, and the environment, can predict what the agent will do next. We are most interested in applying this approach to: expert performance in complex, chaotic, and real-world environments; the cooperation and coordination of multiple experts (i.e., teamwork); and consistencies in the cognitive activities and aptitudes of experts in different domains. Macro Architecture Hypothesis Cognition can be divided into micro and macro cognition (Klein et al, 2003). Micro-cognition refers to the mental activities studied in traditional cognitive psychology experiments, whereas macro-cognition refers to the forms of cognition that underlie functioning in complex, real-world tasks (West et al, 2013; Klein 2003). Concerns have been raised over whether the theories and methods embodied in micro-cognitive experimentation and modeling can be reasonably scaled up out of the lab and applied to the study of real-world cognition (Klein, 2003). One avenue by which this has been addressed is the development of unified cognitive architectures, such as ACT-R (Anderson, 1996), EPIC (Kieras & Meyer, 1997), and SOAR (Laird, Newell,