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Drew McDermott

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

20 papers
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Possible papers

20

JAAMAS Journal 2026 Journal Article

Derivation of Glue Code for Agent Interoperation

  • Mark Burstein
  • Drew McDermott
  • STEPHEN J. WESTFOLD

Abstract Getting agents to communicate requires translating the data structures of the sender (the source representation) to the format required by the receiver (the target representation). Assuming that there is a formal theory of the semantics of the two formats, which explains both their meanings in terms of a neutral topic domain, we can cast the translation problem as solving higher-order functional equations. Some simple rules and strategies apparently suffice to solve these equations automatically. The strategies may be summarized as: decompose complex expressions, replacing topic-domain expressions with source-domain expressions when necessary. A crucial issue is getting the required formal theories of the source and target domains. We believe it is sufficient to find partial formalizations that grow as necessary.

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AIJ Journal 2007 Journal Article

Level-headed

  • Drew McDermott

I don't believe that human-level intelligence is a well defined goal. As the cognitive-science community learns more about thinking and computation, the mileposts will keep changing in ways that we can't predict, as will the esteem we assign to past accomplishments. It would be fun to have a computer that could solve brain teasers as well as the average scientist, but focusing on such things, besides being parochial, overlooks the crucial role language plays in everything humans do, a role we understand hardly at all on a computational level. I am optimistic that we will eventually figure language out, but not without new ideas. Plus, when we can talk to machines, will we understand each other?

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AAAI Conference 1983 Conference Paper

Data Dependencies on Inequalities

  • Drew McDermott

Numerical inequalities present new challenges to data-base systems that keep track of "dependencies," or reasons for beliefs. Care must be taken in interpreting an inequality as an assertion, since occasionally a "strong" interpretation is needed, that the inequality is best known bound on a quantity. Such inequalities often have many proofs, so that the proper response to their erasure is often to look for an alternative proof. Fortunately, abstraction techniques developed by data-dependency theorists are robust enough that they can be extended fairly easily to handle these problems. The key abstractions involved are the "ddnode," an abstract assertion as seen by the data-dependency system, and its associated "signal function," which performs indexing, re-deduction, and garbage-collection functions. Such signal functions must have priorities, so that they don’t clobber each other when they run.

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AAAI Conference 1982 Conference Paper

ARBY: Diagnosis with Shallow Causal Models

  • Drew McDermott

Arby is a software system or higher order language for writing expert systems to do diagnosis in electronic systems. As such, it is similar to EMYCIN (Van Melle 1982) in application, but quite different in design. It is rule-based to an extent, but the rules are written in predicate calculus. It resembles Caduceus (Pople 1977) in its mechanisms for refining and combining hypotheses.

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