Causal Emotion Entailment aims to identify causal utterances that are responsible for the target utterance with a non-neutral emotion in conversations. Previous works limited thorough understanding of conversational context and accurate reasoning cause. To this end, we propose Knowledge-Bridged Interaction Network (KBCIN) commonsense knowledge (CSK) leveraged as three bridges. Specifically, con...

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observ...

We explore the developmental trajectory and underlying mechanisms of relational reasoning. We describe a surprising developmental pattern: Younger learners are better than older ones at inferring abstract relations. Walker and Gopnik (2014) demonstrated that toddlers are able to infer the relations “same” and “different” in a causal system. However, these findings appear to contrast with the li...

In recent years, a number of algorithms have been developed for learning the structure of Bayesian networks from data. In this paper we apply some of these algorithms to a realistic medical domain—stroke. Basically, the domain of stroke is taken as a typical example of a medical domain where much data are available concerning a few hundred patients. Learning the structure of a Bayesian network ...

A key question in Information Systems research is how information technology creates business value. In this paper, our aim is to help reveal the role of alignment between IT and business resources in business value creation. In particular, we propose that the contribution of IT to business process performance should be investigated in the context of actual IT usage, with IT business alignment ...

Lipman [2003] shows that in a finite model, the common prior assumption has weak implications for finite orders of beliefs about beliefs. In particular, the only such implications are those stemming from the weaker assumption of a common support. To explore the role of the finite model assumption in generating this conclusion, this paper considers the finite order implications of common priors ...

Lipman [2003] shows that in a finite model, the common prior assumption has weak implications for finite orders of beliefs about beliefs. In particular, the only such implications are those stemming from the weaker assumption of a common support. To explore the role of the finite model assumption in generating this conclusion, this paper considers the finite order implications of common priors ...

Structural causal models offer a popular framework for exploring causal concepts. However, due to their limited expressiveness, structural models have difficulties coping with such concepts as actual (event-to-event) causation. In this paper, we propose a new type of causal model, based on embedding structural considerations in the language of situation calculus. By using situation calculus as ...