Structure and interpretation of computer programs

By Harold Abelson, Gerald J. Sussman. 1985

From the Publisher: With an analytical and rigorous approach to problem solving and programming techniques,this book is oriented toward engineering. Structure and Interpretation of Computer Programs emphasizes the central role played by different approaches to dealing with time in computational models. Its unique approach makes it appropriate for an introduction to computer science courses,as well as programming languages and program design.

Subfields: Automated Theorem Proving & Formal Methods

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Citations in the corpus

A Compositional Object-Based Approach to Learning Physical Dynamics (2016, methodology): “n the scene as a focus object, and recursively predicts future timesteps using predictions from previous timesteps, employing function reuse to achieve generalization is also featured in work such as Abelson et al. (1996); Andreas et al. (2016); Lake et al. (2015); Reed and de Freitas (2015); Socher”
Automatically Composing Representation Transformations as a Means for Generalization (2018, background): “To do so, one powerful design strategy in constructing programs is to base the structure of the program on the structure of the system being modeled [1]; how does a learner automatically program itself to reﬂect the structure of each problem?”
Church: a language for generative models (2008, background): “…query ; we may describe this as a Church expression: (define (query exp pred env) (let ((val (eval exp env)) (if (pred val) val (query exp pred env))))) The ability to write query as a Church program— a metacircular [1] implementation—provides a compelling argument for Church’s modeling power.”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, methodology): “Although we are still far from a study of the expressiveness of probabilistic languages via definitional interpretation (Reynolds, 1972; Abelson and Sussman, 1983), it seems likely that probabilistic programming formulations of probabilistic program synthesis — inference over a space of probabilisti”
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs (2014, background): “Church is a close relative of the functional programming language Scheme [1].”
DrScheme: a programming environment for Scheme (2002, background): “Scheme’s success is primarily due to Abelson and Sussman’s seminal book (Abelson et al. , 1985) on their introductory course at MIT.”
Revised5 Report on the Algorithmic Language Scheme (1986, methodology): “Note: [2] uses the keyword sequence instead of begin.”
Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? (2011, background): “In 1985, Abelson and Sussman argued that computer science is “a discipline of constructing appropriate descriptive languages” [1].”
Developing Theory Through Simulation Methods (2007, unknown): “Creating a computational representation involves the precise specification of the theoretical logic that is enforced through the discipline of algorithmic representation in software ( Abelson, Sussman, & Sussman, 1996 ).”
Revised6 Report on the Algorithmic Language Scheme (2009, methodology): “An introductory computer science textbook using Scheme was published in 1984 [1].”
Revised4 report on the algorithmic language scheme (1991, methodology): “Note: [2] uses the keyword sequence instead of begin.”
Modeling Cyber–Physical Systems (2012, background): “Abelson and Sussman [1] describe computer science as Bprocedural epistemology,[ knowledge through procedure.”
Meep: A flexible free-software package for electromagnetic simulations by the FDTD method (2010, unknown): “) Another important technique to maintain flexibility is that of higher-order functions [68]: wherever it is practical, our functions”