scholarly journals Representing Causal Information About a Probabilistic Process

2006 ◽  
pp. 452-464 ◽  
Author(s):  
Joost Vennekens ◽  
Marc Denecker ◽  
Maurice Bruynooghe
Keyword(s):  
Causal Information ◽  
Probabilistic Process

The Influence of Causal Information on Treatment Choice

2007 ◽  
Author(s):  
Jennelle E. Yopchick ◽  
Nancy S. Kim
Keyword(s):  
Treatment Choice ◽  
Causal Information

Explanation Beyond Causation

2018 ◽  
Keyword(s):  
Philosophy Of Science ◽  
Case Studies ◽  
Philosophical Study ◽  
Accurate Information ◽  
Contemporary Philosophy ◽  
Causal Information ◽  
Causal Explanations ◽  
Turn On ◽  
Detailed Case ◽  
Wide Range

Explanations are very important to us in many contexts: in science, mathematics, philosophy, and also in everyday and juridical contexts. But what is an explanation? In the philosophical study of explanation, there is long-standing, influential tradition that links explanation intimately to causation: we often explain by providing accurate information about the causes of the phenomenon to be explained. Such causal accounts have been the received view of the nature of explanation, particularly in philosophy of science, since the 1980s. However, philosophers have recently begun to break with this causal tradition by shifting their focus to kinds of explanation that do not turn on causal information. The increasing recognition of the importance of such non-causal explanations in the sciences and elsewhere raises pressing questions for philosophers of explanation. What is the nature of non-causal explanations—and which theory best captures it? How do non-causal explanations relate to causal ones? How are non-causal explanations in the sciences related to those in mathematics and metaphysics? This volume of new essays explores answers to these and other questions at the heart of contemporary philosophy of explanation. The essays address these questions from a variety of perspectives, including general accounts of non-causal and causal explanations, as well as a wide range of detailed case studies of non-causal explanations from the sciences, mathematics and metaphysics.

scholarly journals Building the Foundation of Robot Explanation Generation Using Behavior Trees

2021 ◽  
Vol 10 (3) ◽  
pp. 1-31
Author(s):  
Zhao Han ◽  
Daniel Giger ◽  
Jordan Allspaw ◽  
Michael S. Lee ◽  
Henny Admoni ◽  
...  
Keyword(s):  
Autonomous Robots ◽  
Free Form ◽  
Static Structure ◽  
Simulation Code ◽  
Causal Information ◽  
Robot Behavior ◽  
Behavior Trees ◽  
Robot Task ◽  
Task Specification ◽  
Explanation Generation

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

scholarly journals Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166868 ◽  
Author(s):  
Osvaldo A. Rosso ◽  
Raydonal Ospina ◽  
Alejandro C. Frery
Keyword(s):  
Information Theory ◽  
Causal Information

scholarly journals Children’s Preference for Causal Information in Storybooks

2020 ◽  
Vol 11 ◽  
Author(s):  
Margaret Shavlik ◽  
Jessie Raye Bauer ◽  
Amy E. Booth
Keyword(s):  
Causal Information

A Probabilistic Process Model of Cliff Recession

2001 ◽  
Author(s):  
Mike Walkden ◽  
Jim Hall ◽  
Ian Meadowcroft ◽  
Stuart Stripling
Keyword(s):  
Process Model ◽  
Probabilistic Process

scholarly journals Complexity as causal information integration

2021 ◽  
Author(s):  
Carlotta Langer ◽  
Nihat Ay
Keyword(s):  
Information Integration ◽  
Causal Information

scholarly journals Community Appeal: Explanation without Information

2019 ◽  
Author(s):  
Babak Hemmatian ◽  
Steven A. Sloman
Keyword(s):  
Causal Structure ◽  
Causal Information ◽  
Community Influences ◽  
Domain Generality ◽  
The Common ◽  
The Impact

Formal or categorical explanation involves the use of a label to explain a property of an object or group of objects. In 4 experiments, we provide evidence that label entrenchment, the degree to which a label is accepted and used by members of the community, influences the judged quality of a categorical explanation whether or not the explanation offers substantive information about the explanandum. Experiment 1 shows that explanations using unentrenched labels are seen as less comprehensive and less natural, independent of the causal information they provide. Experiment 2 shows that these intuitions persist when the community has no additional, relevant featural information, so the label amounts to a mere name for the explanandum. Experiment 3 finds a similar effect when the unentrenched label is not widely used, but is defined by a group of experts and the recipient of the explanation is herself an expert familiar with the topic. The effect also obtains for categories that lack a coherent causal structure. Experiment 4 further demonstrates the domain generality of the entrenchment effect and provides evidence against several interpretations of the results. A majority of participants in Experiments 3 and 4 could not report the impact of entrenchment on their judgments. We argue that this reliance on community cues arose because the community often has useful information to provide about categories. The common use of labels as conduits for this communal knowledge results in reliance on community cues even when they are uninformative.

scholarly journals Dispersal across habitat boundaries: uncovering the demographic fates of populations in unsuitable habitat

2021 ◽  
Author(s):  
Megan C. Szojka ◽  
Rachel M. Germain
Keyword(s):  
Temporal Variability ◽  
Adult Life ◽  
Adult Plant ◽  
Suitable Habitat ◽  
Life Stages ◽  
Habitat Types ◽  
The Matrix ◽  
Probabilistic Process ◽  
Habitat Boundaries ◽  
Matrix Patch

AbstractPatchy landscapes are characterized by abrupt transitions among distinct habitat types, forcing species to cross habitat boundaries in order to spread. Since seed dispersal is a probabilistic process, with a kernel that decays with distance, most individuals will fail to reach new, suitable habitat. Although failed dispersers are presumed dead in population models, their demographic fates may not be so simple. If transient survival is possible within unsuitable habitat, then through time, individuals may be able to reach distant, suitable habitat, forming new populations and buffering species from extinction. In a fragmented Californian grassland, we explored the fates of individuals that crossed habitat boundaries, and if those fates differed among specialists dispersing from two habitat types: serpentine habitat patches and the invaded non-serpentine matrix. We surveyed the diversity of seedbank and adult life stages along transects that crossed boundaries between patches and the matrix. First, we considered how patch specialists might transiently survive in the matrix via seed dormancy or stepping-stone populations. Second, we investigated the dispersal of an invasive matrix specialist (Avena fatua) into patches, to assess if sink populations existed across the habitat boundary. We found that dormancy maintained populations of patch specialists deep into the matrix, as abundances of seedbanks and of adult plant communities differed with distance into the matrix. We found evidence that these dormant seeds disperse secondarily with vectors of material flows in the landscape, suggesting that they could eventually reach suitable patches even if they first land in the matrix. We found that A. fatua were largely absent deep in patches, where reproductive outputs plummeted and there was no evidence of a dormant seedbank. Our results not only reveal the demographic fates of individuals that land in unsuitable habitat, but that their ecological consequences differ depending on the direction by which the boundary is crossed (patch → matrix ≠ matrix → patch). Dormancy is often understood as a mechanism for persisting in face of temporal variability, but it may serve as a means of traversing unsuitable habitat in patchy systems, warranting its consideration in estimates of habitat connectivity.

Does Emotion Talk and Emotion Knowledge Effect Children's Recall of a Staged Event?

2021 ◽  
Author(s):  
◽  
Katherine Mackay
Keyword(s):  
Emotion Knowledge ◽  
Causal Information ◽  
Related Information ◽  
Correct Information ◽  
Children's Emotion ◽  
Children's Memory ◽  
Emotion Talk

<p>The current study compared children's memory for information accompanied by emotional or non-emotional talk, and also investigated the utility of emotion knowledge in prediction of recall. Seventy-five children aged 5-6 years participated in a staged event that involved visiting separate stations containing connected, causal information of an emotional or non-emotional theme. Children were assessed with a memory interview one week later. Children reported significantly more correct information from stations with an emotional focus. Children's emotion knowledge did not predict recall, however. Results show children better recall emotion-related information even when causality and connectedness is controlled for. Implications of the finding are discussed.</p>

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