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In response to calls for research to improve human-machine teaming (HMT), we present a “perspective” paper that explores techniques from computer science that can enhance machine agents for human-machine teams. As part of this paper, we (1) summarize the state of the science on critical team competencies identified for effective HMT, (2) discuss technological gaps preventing machines from fully realizing these competencies, and (3) identify ways that emerging artificial intelligence (AI) capabilities may address these gaps and enhance performance in HMT. We extend beyond extant literature by incorporating recent technologies and techniques and describing their potential for contributing to the advancement of HMT.

Problem formulation is an important part of the design process that has been largely underexplored. Similarly, the relationship between how designers formulate problems and creative outcome is not well understood. To shed light on what the process of problem formulation can tell us about creativity in design, we use the problem map model — a flexible, domain-independent ontology for modeling the design formulation process — to analyze protocols from eight expert designers. In this paper, we discuss the effectiveness of using problem maps for coding design protocols and what the problem map model can tell us about the protocols of designers. In this exploratory study, we use the problem map model to code and analyze the problem formulation stage of the design process.

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Abstract. Authoring Intelligent Tutoring Systems is expensive and time consuming. To reduce costs, the Cognitive Tutor Authoring Tools and the Example-Tracing Tutor paradigm were developed to make the tutor authoring process more efficient. Under this paradigm, tutors are constructed by demonstrating behavior directly in a tutor interface, reducing the need for programming expertise. This paper evaluates the efficiency of authoring a tutor with SimStudent, an extension to the ExampleTracing paradigm that is designed to produce greater generality in less time by induction from past demonstrations and feedback. We found that authoring an algebra tutor in SimStudent is faster than ExampleTracing while maintaining equivalent final model quality. Furthermore, we found that the SimStudent model generalizes beyond the problems that were used to author it.