Smart home services for users require precise and reliable context information to guarantee credibility. To do this, system should have ability of context-awareness. Most of the context-aware systems have adopted rule-based inference but it did not fulfill users’ demand. To support valuable services in smart home environment, we present the rulebased inference engine which uses user profiles to...

We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a continuoustime Markov process. This software provides libraries and programs for most of the algorithms developed for CTBNs. For learning, CTBN-RLE implements structure and parameter learning for both complete and partia...

CADIAG-2 is a well known expert system aimed at providing support for medical diagnose in the field of internal medicine. CADIAG-2 consists of a knowledge base in the form of a set of IF-THEN rules that relate distinct medical entities, in this paper interpreted as conditional probabilistic statements, and an inference engine constructed upon methods of fuzzy set theory. The aim underlying this...

This paper reviews eight different inference rules for experience-based reasoning (EBR), and proposes a multiagent architecture for an EBR system, which constitutes an important basis for developing any multiagent EBR systems (EBRS). The proposed architecture consists of a global experience base (GEB), and a multi-inference engine (MIE), which is the mechanism for implementing eight reasoning p...

We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do “compilation of inference” because our method transforms a denotational specification of an infere...

Cadiag2 is a well-known rule-based expert system that aims at providing support for medical diagnose in internal medicine. Cadiag2 consists of a knowledge base in the form of a set of if-then rules that relate medical entities, in this paper interpreted as conditional probabilistic statements, and an inference engine constructed upon methods of fuzzy set theory. The aim underlying this paper is...

This paper presents a new approach to build recommendation systems. Multistrategy Inference and Learning System based on the Logic of Plausible Reasoning (LPR) is proposed. Two groups of knowledge transmutations are defined: inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms to generate intrinsically new knowledge. All oper...

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines—even the compiled ones—incur significant runtime interpretation overhead, especially for cont...

A novel theory of stages in cognitive development is presented, loosely corresponding to Piagetan theory but specifically oriented toward AI systems centered on uncertain inference components. Four stages are articulated (infantile, concrete, formal and reflexive), and are characterized both in terms of external cognitive achievements (a la Piaget) and in terms of internal inference control dyn...