OTTER Experiments in a System of Combinatory Logic

This paper describes some experiments involving the automated theorem-proving program OTTER in the system TRC of illative combinatory logic. We show how OTTER can be steered to find a contradiction in an inconsistent variant of TRC, and present some experimentally discovered identities in TRC

Finding Fixed Point Combinators using Prolog

A Powerful New Strategy, Called the Kernel Method, Has Been Developed by Larry Wos and William McCune at Argonne National Laboratories, to Study Various Fixed-Point Properties within Certain Classes of Applicative Systems. We Present a Very Simple Prolog Reasoning System, Named JIST, Which Incorporates Both Stages of the Kernel Method into a Single Unified Program. Furthermore, the Prolog Tool Has Been Extended to Run within a Distributed Environment using the Linda Protocol

Larry Wos - Visions of automated reasoning

This paper celebrates the scientific discoveries and the service to the automated reasoning community of Lawrence (Larry) T. Wos, who passed away in August 2020. The narrative covers Larry's most long-lasting ideas about inference rules and search strategies for theorem proving, his work on applications of theorem proving, and a collection of personal memories and anecdotes that let readers appreciate Larry's personality and enthusiasm for automated reasoning

A standardisation proof for algebraic pattern calculi

This work gives some insights and results on standardisation for call-by-name pattern calculi. More precisely, we define standard reductions for a pattern calculus with constructor-based data terms and patterns. This notion is based on reduction steps that are needed to match an argument with respect to a given pattern. We prove the Standardisation Theorem by using the technique developed by Takahashi and Crary for lambda-calculus. The proof is based on the fact that any development can be specified as a sequence of head steps followed by internal reductions, i.e. reductions in which no head steps are involved.Comment: In Proceedings HOR 2010, arXiv:1102.346

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates

The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. The resulting algorithm configuration (AC) problem has attracted much attention from the machine learning community. However, the proper evaluation of new AC procedures is hindered by two key hurdles. First, AC benchmarks are hard to set up. Second and even more significantly, they are computationally expensive: a single run of an AC procedure involves many costly runs of the target algorithm whose performance is to be optimized in a given AC benchmark scenario. One common workaround is to optimize cheap-to-evaluate artificial benchmark functions (e.g., Branin) instead of actual algorithms; however, these have different properties than realistic AC problems. Here, we propose an alternative benchmarking approach that is similarly cheap to evaluate but much closer to the original AC problem: replacing expensive benchmarks by surrogate benchmarks constructed from AC benchmarks. These surrogate benchmarks approximate the response surface corresponding to true target algorithm performance using a regression model, and the original and surrogate benchmark share the same (hyper-)parameter space. In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures. We show that our surrogate benchmarks capture overall important characteristics of the AC scenarios, such as high- and low-performing regions, from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate

Strategic factor markets: Bargaining, scarcity, and resource complementarity

Strategic factor market theory suggests that without luck or asymmetric expectations, firms can't appropriate gains from acquired resources. Adopting the bargaining perspective on resource advantage, we hold that this is only true in the absence of resource complementarity. We extend factor market theory to account for resource complementarity, and we show that firms can profit when they exhibit superior complementarity to target resources, even in the absence of asymmetric expectations. Thus we provide an alternative interpretation of managers' recent emphasis on externally acquired resources.Complementarity; bargain perspective; value appropriation; resource acquisition; asymmetric expectation;

Reassessing Constructions in the ARTEMIS Parser

The aim of this study is to reexamine the status of constructions in ARTEMIS (Automatically Representing TExt Meaning via an Interlingua-based System), a Natural Language Understanding prototype that seeks to provide the syntactic and semantic structure of a given fragment in a natural language. The architecture of ARTEMIS has been designed to conform to the tenets of the Lexical Constructional Model (LCM), a theory in which constructions are a central tool for the linguistic description of languages. However, since ARTEMIS is a computational device, there are many formalization requirements which involve the adaptation of the LCM, a process which necessarily leads to reconsidering several issues, as are: (i) what counts as a constructional structure; (ii) how constructions contribute to parsing operations in ARTEMIS; and (iii) the location and the format of constructional patterns.