Probabilistic Programming Concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years

Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

Probabilistic (logic) programming concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years

Tackling benchmark problems for commonsense reasoning.

Abstract. There is increasing interest in the field of automated commonsense reasoning to find real world benchmarks to challenge and to further develop reasoning systems. One interesting example is the Triangle Choice of Plausible Alternatives (Triangle-COPA), which is a set of problems presented in first-order logic. The setting of these problems stems from the famous Heider-Simmel film used in early experiments in social psychology. This paper illustrates with two logical approachesabductive logic programming and deonitc logic-how these problems can be solved. Furthermore, we propose an idea of how to use background knowledge to support the reasoning process

Exploiting World Knowledge inDiscourse Processing – A Comparison of Feature -Based and Inference-Based Approaches–

Tohoku University乾健太郎課

自然言語談話解析のための大規模かつ学習可能な仮説推論に関する研究

Tohoku University乾健太郎課