This paper presents a theory for role-playing simulation games intended to support analysts (and trainees) with generating and testing alternative competing hypotheses on how to influence world conflict situations. Simulated leaders and followers capable of playing these games are implemented in a cognitive modeling framework, called the Performance Moderator Function Server (PMFserv), which covers value systems, personality and cultural factors, emotions, relationships, perception, stress/coping style, and decision making. Of direct interest, asSection I-Aexplains, is codification and synthesis of best-of-breed social-science models within PMFserv to improve the internal validity of agent implementations.Sections IIandIIIpresent this for leader profiling instruments and group-membership decision making, respectively.Section IVthen offers two real-world case studies (The Third Crusade and SE Asia Today) where agent models are subjected to Turing and correspondence tests under each case study. In sum, substantial effort on game realism, best-of-breed social-science models, and agent validation efforts is essential if analysis and training tools are to help explore cultural issues and alternative ways to influence outcomes. Such exercises, in turn, are likely to improve the state of the science as well.

This research explores ways of harnessing people's passion for entertainment in order to stimulate players to attain the meta-learning skills they need for lifelong learning and to reach the highest level of learning, that of learning by teaching. More specifically to keep up with today's pace of training and the goal for it to be just-in-time, we are interested in creating a game generator to empower grass roots experts to share their stories in such a way that others learn the lessons by engagement and empathy with the storyworld characters. Doing so requires us to address the fundamental issue that caused the entertainment and educational gaming communities to split: that it is not easy to create something fun while constrained by rigid pedagogical goals. We examine a case study of the attempts of one cross-disciplinary team to overcome aesthetic silos, and propose a critical systems methodology and generator that harness the core mechanics that make gaming fun, while providing an accessible means for educators to create content to deliver the educational payload.

This paper describes efforts to develop and field an agent-based, healthcare middleware framework that securely connects practice rule sets to patient records to anticipate health todo items and to remind and alert users about these items over the web. Reminders and todos, too (R2Do2) is an example of merging data- and document-centric architectures, and of integrating agents into patient-provider collaboration environments. A test of this capability verifies that R2Do2 is progressing toward its two goals: (1) an open standards framework for middleware in the healthcare field; and (2) an implementation of the 'principle of optimality' to derive the best possible health plans for each user. This paper concludes with lessons learned to date.

This article offers a model of collaboration processes in which both parties are sharing the task work load at an equal level of cognitive difficulty. The model poses six collaboration factors as important in the man-machine collaboration. The six factors are cognitive orientation, deep knowledge, intention sharing, control plasticity, adaptivity, and experience or memory. The model predicts that two clusters of settings of the six factors exist: one for novices and one for experts. Four experiments are presented that support this prediction and that offer several new insights into what makes for effective collaborator design. Also many new questions arise.

In trying to solve even small problems, humans often form wrong hypotheses by looking only at the most salient factors and by ignoring contrary evidence. Once they form their initial beliefs, they exhibit a strong tendency to seek only additional confirming evidence. This type of behavior is the confirmation bias. Empirical evidence is presented suggesting that computers can interact with humans by qualitatively modeling and detecting their confirmation bias and by offering criticism strategies that will effectively mitigate the unwanted effects. The degree of improvement is sensitive to the strategy and content of the critic. A number of strategies are constructed and evaluated, and it is shown which ones are most likely to lead to success for varying circumstances and user types. Additional fruitful research directions are pinpointed.>

The authors review the status of a generic blackboard-based distributed problem-solving environment in which multiple agent cooperation can be effected. This environment is organized into a shared information panel, a chairman control panel, and a metaplanning panel. Each panel, in turn, contains a number of embedded AI (artificial-intelligence) techniques that facilitate its operation and that provide heuristics for solving the underlying team-agent decision problem. The status of these panels and heuristics is described along with a number of robustness considerations. The techniques of each of the three panels and of four sets of paradigm-related advances are described, along with selected results from classroom teaching experiments and from three applications using BSG to date.>

The failure of traditional expert system algorithms and mathematics in real-time tasks is described, and an alternative inference engine design more appropriate to real-time problem solving is explored. The approach is generic, but to facilitate its description an example is used involving human supervisory operators of a semiautomated telecommunication network. Each human's real-time process control tasks are first decomposed into several types of subtasks under cases of certain, uncertain, and conflicting information. Two divergent calculi are seen to be required for these various tasks: 1) the "situational calculus" for real-time deterministic control, and 2) the "calculus of uncertainty" for fusion of conflicting information from diverse knowledge sources and for propagating uncertainty through the inference net to reach conclusions that can be executed by the situational calculus. The distributed architecture ramifications of a dual-calculus appoach are explained, and the fusion technique is elaborated.

A human information processing account of how experienced practitioners utilize analogues in the innovation process is presented. The protocols of several practitioners are collected and analyzed. The problem behavior graphs, knowledge elements utilized, and operator elements applied are described, and a descriptive model of the innovation process is assembled. Implications for a production system, deviations from other models of the innovation process, and extensions to other models of the analogy procedure are explored.

This theoretical analysis of the intuitive and diffuse characteristics of analogical reasoning processes is the first step in a research effort intended to lead to: understanding of common (and possibly costly) errors, pitfalls, travails, and problem-solving impediments; possible recommendations for improvements to organizational structures, control and coordination processes, and management information flows, and guidelines for a generalized analogical reasoning support framework (e.g., a handbook, a knowledge bank design, and/or even a software package/artificial intelligence program).