Existing research on category-based induction has primarily focused on reasoning about blank properties, or predicates that are designed to elicit little prior knowledge. Here, we address reasoning about nonblank properties. We introduce a model of conditional probability that assumes that the conclusion prior probability is revised to the extent warranted by the evidence in the premise. The de...

Planning under uncertainty requires the adoption of assumptions about the current and future states of the world, and the preparation of conditional plans based on these assumptions. In any realistic domain, however, there will be an exponential explosion in the number of conditional plans required. One approach to this problem is to articulate a set of scenarios, which together are representat...

OBJECTIVE The purpose of this study was to explore current conceptualizations of clinical reasoning in occupational therapy. METHOD Using a head-mounted video camera and debriefing interviews following therapy sessions, clinical reasoning data were collected from 13 experienced occupational therapists working in physical rehabilitation. The data were collected and analyzed within a focused et...

cplint is a suite of programs for reasoning and learning with Probabilistic Logic Programming languages that follow the distribution semantics. In this paper we describe how we have extended cplint to perform causal reasoning. In particular, we consider Pearl’s do calculus for models where all the variables are measured. The two cplint modules for inference, PITA and MCINTYRE, have been extende...

Many real life optimization problems are defined in terms of both hard and soft constraints, and qualitative conditional preferences. However, there is as yet no single framework for combined reasoning about these three kinds of information. In this paper we study how to exploit classical and soft constraint solvers for handling qualitative preference statements such as those captured by the CP...

Possibilistic logic has been proposed as a nu­ merical formalism for reasoning with uncertainty. There has been interest in developing qualitative accounts of possibility, as well as an explana­ tion of the relationship between possibility and modal logics. We present two modal logics that can be used to represent and reason with qual­ itative statements of possibility and necessity. Within thi...

The graphoid axioms for conditional independence, originally described by Dawid [1979], are fundamental to probabilistic reasoning [Pearl, 1988]. Such axioms provide a mechanism for manipulating conditional independence assertions without resorting to their numerical definition. This paper explores a representation for independence statements using multiple undirected graphs and some simple gra...

Markov networks utilize nonembedded probabilistic conditional independencies in order to provide an economical representation of a joint distribution in uncertainty management. In this paper we study several properties of nonembedded conditional independencies and show that they are in fact equivalent. The results presented here not only show the useful characteristics of an important subclass ...

Independence – the study of what is relevant to a given problem of reasoning – is an important AI topic. In this paper, we investigate several notions of conditional independence in propositional logic: Darwiche and Pearl’s conditional independence, and two more restricted forms of it, called strong conditional independence and perfect conditional independence. Many characterizations and proper...

We investigate the idea of representing conditional measures as simple measures (possibly with further properties) on conditional objects. To formalise this intuition we introduce a logico-algebraic framework based on the concept of conditional algebras. These are structures which allow us to distinguish between the logical properties of conditionals and those of conditional measures, a distinc...