Demo Data from EDBT 2026 / reviewers in the wild / expert
Brian Cantwell Smith
dblp:90/4931
· DBLP profile ↗
9ranked-venue papers
4as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 2 first-authorSoftware engineering, systems software and programming languages · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3Theory of computation · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
3 papers |
Programming languages and type systems · 68% Empirical software engineering · 13% Concurrent programming · 11% | |
| Artificial intelligence
5 papers |
Knowledge representation and reasoning · 74% Information extraction and text analysis · 26% |
Topics — the 13 heaviest of 14, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
expert systems |
0.0 | 1 | 1985 | Expert Systems: How Far Can They Go? · IJCAI 1985 |
Programming languages and type systems
language semantics |
0.0 | 1 | 1984 | Reflection and Semantics in Lisp · POPL 1984 |
Programming languages and type systems › metaprogramming
reflection |
0.0 | 1 | 1984 | Reflection and Semantics in Lisp · POPL 1984 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › neuro-symbolic reasoning
symbol grounding |
0.0 | 1 | 1983 | Under What Conditions Can a Machine Attribute Meanings to Symbols · IJCAI 1983 |
Natural language and speech › Information extraction and text analysis › natural language semantics
computational semantics |
0.0 | 1 | 1982 | Linguistic and Computational Semantics · ACL 1982 |
Empirical software engineering › software engineering research methodology
meta-evaluation |
0.0 | 2 | 1975 | Towards a Programming Apprentice · IEEE Trans. Software Eng. 1975 Actor Induction and Meta-Evaluation · POPL 1973 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge-based systems |
0.0 | 1 | 1985 | Expert Systems: How Far Can They Go? · IJCAI 1985 |
Programming languages and type systems
language design |
0.0 | 1 | 1984 | Reflection and Semantics in Lisp · POPL 1984 |
Programming languages and type systems › functional programming
lisp |
0.0 | 1 | 1984 | Reflection and Semantics in Lisp · POPL 1984 |
Requirements engineering and software design › specification
software contracts |
0.0 | 1 | 1975 | Towards a Programming Apprentice · IEEE Trans. Software Eng. 1975 |
Natural language and speech › Information extraction and text analysis
natural language semantics |
0.0 | 1 | 1982 | Linguistic and Computational Semantics · ACL 1982 |
Concurrent programming › concurrency models
actor model |
0.0 | 1 | 1973 | Actor Induction and Meta-Evaluation · POPL 1973 |
Concurrent programming
message passing |
0.0 | 1 | 1973 | Actor Induction and Meta-Evaluation · POPL 1973 |
Methods — techniques the papers use, named apart from their topics
actor model · 0.0knowledge representation · 0.0justification · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | The Owl and the Electric EncyclopediaabstractA review of “On the thresholds of knowledge”, by D.B. Lenat and E.A. Feigenbaum. Brian Cantwell Smith |
Artif. Intell. | 1 |
| 1986 | Varieties of Self-Reference
Brian Cantwell Smith |
TARK | 1 |
| 1985 | Expert Systems: How Far Can They Go?
Terry Winograd, Randall Davis, Stuart Dreyfus, Brian Cantwell Smith |
IJCAI | 4 |
| 1984 | Reflection and Semantics in Lisp
Brian Cantwell Smith |
POPL | 1 |
| 1983 | Under What Conditions Can a Machine Attribute Meanings to Symbols
Aaron Sloman, Drew McDermott, William A. Woods, Brian Cantwell Smith, Patrick J. Hayes |
IJCAI | 4 |
| 1982 | Linguistic and Computational SemanticsabstractWe argue that because the very concept of computation rests on notions of interpretation, the semantics of natural languages and the semantics of computational formalisms are in the deepest sense the same subject. The attempt to use computational formalisms in aid of an explanation of natural language semantics, therefore, is an enterprise that must be undertaken with particular care. We describe a framework for semantical analysis that we have used in the computational realm, and suggest that it may serve to underwrite computationally-oriented linguistic semantics as well. The major feature of this framework is the explicit recognition of both the declarative and the procedural import of meaningful expressions; we argue that whereas these two viewpoints have traditionally been taken as alternative, any comprehensive semantical theory must account for how both aspects of an expression contribute to its overall significance. Brian Cantwell Smith |
ACL | 1 |
| 1977 | Knowledge Representation
Daniel G. Bobrow, Gary G. Hendrix, William A. Martin, John McCarthy 0001, Allen Newell, Roger C. Schank, Brian Cantwell Smith |
IJCAI | 7 |
| 1975 | Towards a Programming ApprenticeabstractThe Planner Project is constructing a programming apprentice to assist in knowledge based programming. The programming apprentice is designed for interactive use by expert programmers in the meta-evaluation of implementations in the context of their contracts and background knowledge. Meta-evaluation produces a justification which makes explicit exactly how the module depends on the contracts of other modules and on the background knowledge. The justification is used in answering questions on the behavioral dependencies between modules and in analyzing the implications of perturbations in specifications and/or implementation. Karl E. Hewitt, Brian Cantwell Smith |
IEEE Trans. Software Eng. | 2 |
| 1973 | Actor Induction and Meta-EvaluationabstractThe PLANNER project is continuing research in natural and effective means for embedding knowledge in procedures. In the course of this work we have succeeded in unifying the formalism around one fundamental concept: the ACTOR. Intuitively, an ACTOR is an active agent which plays a role on cue according to a script. We use the ACTOR metaphor to emphasize the inseparability of control and data flow in our model. Data structures, functions, semaphores, monitors, ports, descriptions, Quillian nets, logical formulae, numbers, identifiers, demons, processes, contexts, and data bases can all be shown to be special cases of actors. All of the above are objects with certain useful modes of behavior. Our formalism shows how all of these modes of behavior can be defined in terms of one kind of behavior: sending messages to actors. An actor is always invoked uniformly in exactly the same way regardless of whether it behaves as a recursive function, data structure, or process. Carl Hewitt, Peter Boehler Bishop, Irene Greif, Brian Cantwell Smith, Todd Matson, Richard Steiger |
POPL | 4 |