Inference Control

Introduction

Every reasoning system has a "logic" part and a "control" part. The former specifies what can be expressed (the grammar and semantics) and what can be derived in each step (the inference rules), while the latter specifies what tasks can be carried out by linking the inference steps into inference processes. This relation is put into an intuitive formula by Robert Kowalski as "Algorithm = Logic + Control", though it can be extended beyond algorithmic processes.

The logic part of OpenNARS consists of the formal language Narsese, Experience-Grounded Semantics (EGS), and Non-Axiomatic Logic (NAL). The following is an overview of the control mechanism of OpenNARS.

Design principle

Traditionally, the control mechanism in a reasoning system depends on either a domain-specific "strong method" or a domain-independent "weak method". In the former case, the inference steps are organized into inference algorithms specially designed for the problem to be solved; in the latter case, some universal algorithm, such as exhaustive research, is applied that does not need domain-specific knowledge. In both cases, the inference process follows an algorithm that is repeatable, either deterministically or probabilistically.

OpenNARS is based on the theory that intelligence is relative rationality, and an intelligent system should be adaptive and work with insufficient knowledge and resources. Consequently, OpenNARS uses neither the "strong method" nor the "weak method", but works in a "non-algorithmic" manner, by selecting its inference steps at run time, depending on many ever-changing factors. Consequently, an individual inference process, as part of the system's lifelong experience, is not repeatable, even in the probabilistic sense. It is not a "strong method" because OpenNARS is designed to be general-purpose, without assuming any domain knowledge. On the other hand, it is not "weak method", neither, because the system learns domain knowledge gradually, so its selection of steps will become more and more based on its past experience.

Roughly speaking, the function of the control mechanism in OpenNARS is to dynamically allocate the system's limited resources to the activities demanding them. The objective is to optimize the expected overall efficiency, estimated according to the system's beliefs summarizing the past experience. Under the assumption of insufficient resources, the system will not be able to satisfy all the resource demands, and under the assumption of insufficient knowledge, there is no guarantee that the selections the system makes are really optimal when judged according to the system's future experience.

Formal models of resource allocation

See here