Adelfa is an interactive proof assistant for reasoning about specifications written in the Edinburgh Logical Framework (LF). Adelfa is based on a new logic named 𝓛LF whose atomic formulas represent typing judgements in LF and in which complex formulas can be constructed using the usual collection of propositional connectives and quantifiers over variables representing LF terms and contexts. Quantification over terms is qualified by arity types that indicate functional structure but that leave dependency information to the domain of the body of the formula. Context quantification is governed by a special kind of type, referred to as a context schema, that describes a finite collection of patterns of declarations out of which an instantiating (ground) context may be constructed. The meaning of formulas in this logic is described by using derivability in LF to determine the validity of ground atomic formulas and attributing a substitution semantics to the quantifiers. Complementing the logic is a proof system that provides the basis for establishing the validity of formulas in the logic. Particular proof rules in this proof system embody a means for carrying out case analyses relative to LF derivability and for reasoning inductively over the heights of LF derivations. The logic also provides a flexible basis for additional rules, such as ones that encode meta-theorems related to derivability in LF. Adelfa implements a collection of such rules as well.
An expository presentation of Adelfa can be found in the paper entitled "Adelfa: A system for reasoning about LF specifications." The reader may also consult a walkthough of a development that we have provided on this page and also some of the small, but growing, number of reasoning examples that are currently available. A detailed theoretical development of the logic underlying Adelfa is to be found in the paper entitled "A logic for reasoning about LF specifications."
The current working version of Adelfa can be obtained by downloading this tarball.
Work on Adelfa was supported by the National Science Foundation under Grant No. CCF-1617771. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.