It has generally been assumed that the knowledge employed by an AI reasoning system needs to be in an unambiguous, formally interpretable form. From that perspective, general knowledge expressed in ordinary language (e.g., “dogs bark”) is unacceptably ambiguous and incomplete. However, we can achieve at least a partial transformation of such knowledge into formal, generically quantified sentenc...

We investigate connectionist models of rule-based reasoning, and show that while such models usually carry out reasoning in exactly the same way as symbolic systems, they have more to ooer in terms of commonsense reasoning. A connection-ist architecture for commonsense reasoning, CON-SYDERR, is proposed to account for common-sense reasoning patterns and to remedy the brit-tleness problem in tra...

The purpose of this article is threefold: to present a general framework for investigating processes of reasoning and problem solving in market-related situations that are not transparent, to present some central processes involved in sensemaking, and to present a set of key elements of abductive reasoning. The paper focuses on the processes involved in making sense, on the on-line generation o...

A nonmonotonous reasoning model to decision making under fuzzy exceptional conditions is suggested in this paper. This model provides a tool to process incomplete, uncertain or variable information by using production rules with exceptions while attempting to model human reasoning in the field of common sense. Explicit processing of exceptional conditions reduces probability of the incorrect de...

A connectionist model for robust reasoning, CONSYDERR, is proposed to account for some common reasoning patterns found in commonsense reasoning and to remedy the brittleness problem. A dual representational scheme is devised, which utilizes both localist representation and distributed representation with features. We explore the synergy resulted from the interaction between these two types of r...

Semiquantitative simulation is a novel approach for the analysis of uncertain dynamic systems that performs a comprehensive simulation study based on automated reasoning methods (for example with the SQSIM algorithm). Semiquantitative simulation of complex models is, however, hindered by the limited automated reasoning capabilities of the currently available semiquantitative simulation techniqu...

Cyc is the largest existing common-sense knowledge base. Its ontology makes heavy use of higher-order logic constructs such as a context system, first class predicates, etc. Many of these higher-order constructs are believed to be key to Cyc’s ability to represent common-sense knowledge and reason with it efficiently. In this paper, we present a translation of a large part (around 90%) of the C...

In this paper, we propose a logic framework for meta level reasoning as well as default reasoning in a general sense, based on an arbitrary underlying logic. In this framework, meta level reasoning is the task of how to deduce new meta level rules by giving a set of rules, whilst default reasoning is the problem of what are the possible candidate beliefs by giving them. We define the semantics ...

Qualitative spatial reasoning (QSR) pursues a symbolic approach to reasoning about a spatial domain. Qualitative calculi are defined to capture domain properties in relation operations, granting a relation algebraic approach to reasoning. QSR has two primary goals: providing a symbolic model for human common-sense level of reasoning and providing efficient means for reasoning. In this paper, we...