One of the original motivations for research in qualitative physics was the development of intelligent tutoring systems and learning environments for physical domains and complex systems. This article demonstrates how a synergistic combination of qualitative reasoning and other AI techniques can be used to create an intelligent learning environment for students learning to analyze and design thermodynamic cycles. Pedagogically this problem is important because thermodynamic cycles express the key properties of systems which interconvert work and heat, such as power plants, propulsion systems, refrigerators, and heat pumps, and the study of thermodynamic cycles occupies a major portion of an engineering student's training in thermodynamics. This article describes CyclePad, a fully implemented articulate virtual laboratory that captures a substantial fraction of the knowledge in an introductory thermodynamics textbook and provides explanations of calculations and coaching support for students who are learning the principles of such cycles. CyclePad employs a distributed coaching model, where a combination of on-board facilities and a server-based coach accessed via email provide help for students, using a combination of teleological and case-based reasoning. CyclePad is a fielded system, in routine use in classrooms scattered all over the world. We analyze the combination of ideas that made CyclePad possible and comment on some lessons learned about the utility of various AI techniques based on our experience in fielding CyclePad. 1999 Elsevier Science B.V. All rights reserved.

this paper examines these relationships in more detail, and asks whether contributions, perceived benefits, and the relationships among them were different for owners of the lists (formal leaders), active posters, and lurkers of the groups, and for nonwork-related and work related groups. To test our hypotheses, we conducted repeated measures ANOVAs with respondent role (owner or other member) and group type (non-work or work-related) as fixed effects, and group size and content volume as covariates. Building 18 2 3 4 5 6 7 8 9 10 11 12 13 14 1 . T o t a l T i m e 1 . 0 0 2. Infrastructure Maintenance . 6 8 * * 1 . 0 0 3. Social Control .29** .28** 1.00 4. Social Encouragement .42** .29** .47** 1.00 5. External Promotion .31** .33** .29** .32** 1.00 6. Content Provision .87** .65** .29** .36** .28** 1.00 7. Audience Engagement .39** .11* .12* .16** .06 . 24** 1.00 9. Information Benefits .16** .05 -.10 .13* .00 .06 .15** .24** 1.00 10. Social Benefits .33** .17** .20** .35** .22** .28** .17** .30** .30** 1.00 11. Altruistic Benefits .28** .23** .10 .34** .15** .26** .13* .32** .28** .49** 1.00 12. Work-Related Group -.06 .07 -.09 .04 -.02 -.03 -.02 .28** .08 -.12* .09 1.00 13. Log (Group Size) -.01 -.11 -.02 .02 -.09 -.11* .10 .07 .20** -.08 -.05 .17** 1.00 14. Log (Message Volume + .01) .19** -.04 .08 .13* -.03 .08 .13* .06 .21** .08 .09 -.18** .52** 1.00 15. # of Members Known Outside the Group .14** .20** .06 .08 .07 .31** -.01 .06 -.06 .02 .12* .05 -.07 .01 Pairwise Ns range from 325 to 385 * p <= 0.05; ** p <= 0.01 Table 3: Correlations among measures 19 RESULTS A premise of this research is that community building requires significant expenditures of time and effort on the part of members. The descriptive analysis presented below shows that members reported inv...

Several barriers hinder the widespread application of AI-based educational software. School and student machines are often underpowered, keeping software and case libraries updated can be difficult, and customization typically requires AI expertise. The widespread growth of Internet access, combined with appropriate AI technologies, enables the creation of distributed coaches that can help overcome these barriers. We describe a distributed coaching system for a deployed intelligent learning environment in engineering thermodynamics. Part of the coach resides on the student's computer, with the rest residing in a server accessed via email. The on-board coach handles common kinds of contradictions in student's assumptions and makes suggestions about parameter values based on its understanding of the teleology of the student's design, derived via Bayesian reasoning. The email coach provides additional analysis help and uses analogy for design coaching, providing step-by-step advice on how principles in a web-based library can be applied to a student's particular design. The distributed coach is currently undergoing field testing.

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We describe the Open Mind Initiative, a framework for large-scale collaborative e#orts in building components of "intelligent" systems that address common-sense reasoning, document and language understanding, speech and character recognition, and so on. Based on the Open Source methodology, the Open Mind Initiative allows domain specialists to contribute algorithms, tool developers to provide software infrastructure and tools, and non-specialist "e-citizens" to contribute training data and information to large databases. An important challenge is to make it easy and rewarding for e-citizens to provide such information. This paper illustrates the Initiative through several demonstration projects of modest scale, including some related to character and document problems, and identifies general challenges and opportunities.

hx ?self ?in ?out) (thermodynamic-stuff ?in) (thermodynamic-stuff ?out) (total-fluid-flow ?in ?out) (== (mass-flow ?in) (mass-flow ?out)) (parameter (mass-flow ?self)) (parameter (Q ?self)) (parameter (spec-Q ?self)) (heat-source (heat-source ?self)) ((parts :cycle) has-member ?self) (?self part-names (in out)) (?self IN ?in)(?in IN-OF ?self) ?self out ?out)(?out out-of ?self)) (defAssumptionClass ((abstract-Hx ?hx ?in ?out)) (isobaric ?hx) (:not (isobaric ?hx))) (defEntity (Heater ?self ?in ?out) (abstract-Hx ?self ?in ?out) (?self instance-of heater) (heat-flow (heat-source ?self) (heat-source ?self) ?in ?out) ((heat-flows-in :cycle) has-member (Q ?self)) (> (Q ?self) 0.0)) (defEquation Hx-law ((Abstract-Hx ?hx ?in ?out)) (:= (spec-h ?out) (+ (spec-h ?in) (spec-Q ?hx)))) (defEquation spec-Q-definition ((Abstract-Hx ?hx ?in ?out)) (:= (spec-Q ?hx) (/ (Q ?hx) (mass-flow ?hx)))) 3.4.1 CyclePad's Knowledge Base The domain knowledge in CyclePad is represented using techniques from qualitative physics (Forbus, 1984) and compositional modeling (Falkenhainer & Forbus, 1991). The knowledge required to support design and analysis goes far beyond just a set of equations, as the examples in Figure 7 illustrate. CyclePad's domain theory includes: . Physical and conceptual entities: These include components such as compressors, turbines, pumps, and heat exchangers; physical processes such as compression, combustion, and expansion, and the representations of the properties of the working fluid between them. CyclePad's knowledge base currently contains over 29 entity definitions.