The four dominant theories of reasoning from conditionals are translated into formal models: The theory of mental models (Johnson-Laird, P. N., & Byrne, R. M. J. (2002). Conditionals: a theory of meaning, pragmatics, and inference. Psychological Review, 109, 646-678), the suppositional theory (Evans, J. S. B. T., & Over, D. E. (2004). If. Oxford: Oxford University Press), a dual-process variant...

Abductive reasoning is a powerful inference mechanism that can generate conditional proofs. The combination of Abduction and Logic Programming (ALP) [5] has many known applications, such as planning, scheduling, cognitive robotics, medical diagnosis and policy analysis [3]. However, most abductive frameworks [7, 4, 8] rely on a centrally executed proof procedure whereas many of the application ...

Conditional probabilities are a core concept in machine learning. For ex-ample, optimal prediction of a label Y given an inputX corresponds to maximizingthe conditional probability of Y given X . A common approach to inference tasksis learning a model of conditional probabilities. However, these models are oftenbased on strong assumptions (e.g., log-linear models), and hence the...

Modeling human conditional reasoning of the type “if p then q” containing negations poses a challenge for connectionism. An integrate-and-fire pulsed network (INFERNET) was used to model this type of conditional reasoning. This model also provides an insight on certain human limitations. The model is compared to empirical data, and classical explanations. Statistical analysis shows that the mod...

We present the Maude sufficient completeness tool, which explicitly supports sufficient completeness reasoning for partial conditional specifications having sorts and subsorts and with domains of functions defined by conditional memberships. Our tool consists of two main components: (i) a sufficient completeness analyzer that generates a set of proof obligations which, if discharged, ensures su...

A notion of quantified conditional logics is provided that includes quantification over individual and propositional variables. The former is supported with respect to constant and variable domain semantics. In addition, a sound and complete embedding of this framework in classical higherorder logic is presented. Using prominent examples from the literature it is demonstrated how this embedding...

In a series of papers, Liu, Lakemeyer, and Levesque address the problem of decidable reasoning in expressive first-order knowledge bases. Here, we extend their ideas to accommodate conditional beliefs, as in “if she is Australian, then she presumably eats Kangaroo meat.” Perhaps the most prevalent semantics of a conditional belief is to evaluate the consequent in the most-plausible worlds consi...

Fuzzy qualitative reasoning (FQR) is a form of approximate reasoning that can be defined loosely as the fusion of Fuzzy Reasoning (FR) with Qualitative Reasoning (QR). Both these research areas have as one of their goals the construction of computational reasoning tools that can predict and explain the behaviour of, often dynamic, systems whose analytic relations are incompletely specified. Whe...

Interactive epistemology in dynamic games studies forms of strategic reasoning like backward and forward induction by means of a formal representation of players’ beliefs about each other, conditional on each history. Work on this topic typically relies on epistemic models where states of the world specify both strategies and beliefs. Strategies are conjunctions of behavioral conditionals of th...