For more effective collaboration, users and autonomous systems should interact naturally. We propose that sketch-based interaction coupled with qualitative representations and analogy provides a natural interface for users and systems. We introduce comic graphs that capture tasks in terms of the temporal dynamics of the spatial configurations of relevant objects. This paper demonstrates, through a strategy simulation example, how these models could be learned by demonstration, transferred to new situations, and enable explanations.

While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research in using natural English text queries and automated deduction to obtain answers based on multiple structured data sources in a specific subject domain. Each English query is transformed using natural language technology into an unambiguous logical form; this is submitted to a theorem prover that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in external databases known to the system. An answer is obtained from the proof, along with an English language explanation of how the answer was obtained. Answers need not be present explicitly in any of the databases, but rather may be deduced or computed from the information they provide. Although English is highly ambiguous, the natural language technology is informed by subject domain knowledge, so that readings of the query that are syntactically plausible but semantically impossible are discarded. When a question is still ambiguous, the system can interrogate the patient to determine what meaning was intended. Additional queries can clarify earlier ones or ask questions referring to previously computed answers. We describe a prototype system, Quadri, which answers questions about HIV treatment using the Stanford HIV Drug Resistance Database and other resources. Natural language processing is provided by PARC’s Bridge, and the deductive mechanism is SRI’s SNARK theorem prover. We discuss some of the problems that must be faced to make this approach work, and some of our solutions.

The most important challenge facing AI today is enabling components to interact in larger scale systems, where modules built with multiple alternative methodologies can be incorporated into robust applications.

Complex electro-mechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. This has become an accepted part of the practice of Xerox, and the control software is deployed in high-end Xerox printers.

Complex electro-mechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. The first is how to make the overall control architecture adapt to, and use productively, the inclusion of particular modules. The second is to decide, at design time, whether a proposed module is a worthwhile addition to the system: will the resulting system perform enough better to outweigh the costs of including the module? This article indicates how the use of qualitative, constraint-based models provides support for solving both of these problems. This has become an accepted part of the practice of Xerox, and the control software is deployed in high-end Xerox printers.

Each is supported and work to find a general solution to that by a formalized theory of the moves in the class of problems.

Letters to the editor on the lack of a central index to the field's published works and the fact that many original works are not published in journals; praise for Letovsky article -- stimulating and amusing. felt subsequent letters to editors were full of bombastic indignation; criticism of Kasday letter about it and Bob Engelmore's weak support of the article; dualism in regards to Letovsky letter; and a reply to criticism by Letovsky, acknowledging diaristic form.

Many of the ideas behind object-oriented programming have roots going back to SIMULA. The first substantial interactive, display-based implementation was the SMALLTALK language. The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. It is also related to a line of work in AI on the theory of frames and their implementation in knowledge representation languages such as KRL, KEE, FRL, and UNITS.

Many of the ideas behind object-oriented programming have roots going back to SIMULA. The first substantial interactive, display-based implementation was the SMALLTALK language. The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. The history of ideas has some additional threads including work on message passing as in ACTORS, and multiple inheritance as in FLAVORS. It is also related to a line of work in AI on the theory of frames and their implementation in knowledge representation languages such as KRL, KEE, FRL, and UNITS.

Early this year fifty people took an experimental course at Xerox PARC on knowledge programming in Loops. During the course, they extended and debugged small knowledge systems in a simulated economics domain called Truckin. Everyone learned how to use the environment Loops, formulated the knowledge for their own program, and represented it in Loops. The punchline to this story is that almost everyone learned enough about Loops to complete a small knowledge system in only three days.