Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.

/pdf/combination-of-evidence-in-dempster-shafer-theory-244gn8j3se.pdf

Combination of Evidence in Dempster-Shafer Theory

A new distance between two bodies of evidence

Plan recognition is an important task whenever a system has to take into account an agent's actions and goals in order to be able to react adequately. Most plan recognizers work by merely maintaining a set of equally plausible plan hypotheses each of which proved compatible with recent observations without taking into account individual preferences of the currently observed agent. Such additional information provides a basis for ranking the hypotheses so that the “best” one can be selected whenever the system is forced to react (e.g., to provide help to the user of a software system to accomplish his goals). Furthermore, hypotheses with low valuations can be excluded from considerations at an early stage. In this paper, an approach to the quantitative modeling of the required agent-related data and their use in plan recognition is presented. It relies on the DempsterShafer Theory and provides mechanisms for the initialization and update of corresponding numerical values.

A dempster-shafer approach to modeling agent preferences for plan recognition

We introduce a logic-based system which improves the performance of intelligent help systems by supplying them with plan generation and plan recognition components. Both components work in close mutual cooperation There are two modes of cross-talk between them, one where plan recognition is done on the basis of abstract plans provided by the planner and the other where optimal plans are generated based on recognition results The examples which are presented are taken from an operating system domain, namely from the UNIX mail domain.

/pdf/phi-a-logic-based-tool-for-intelligent-help-systems-39kqj04zda.pdf

PHI: a logic-based tool for intelligent help systems

Software agents are intended to autonomously perform certain tasks on behalf of their users. In many cases, however, the agent's competence might not be sufficient to produce the desired outcome. Instead of simply giving up and leaving the whole task to the user, a much better alternative is to precisely identify what the cause of the current problem is, communicate it to another agent who can be expected to be able (and willing) to help, and use the results to carry on with achieving the original goal. An ideal candidate for the role of such a supporting agent is the user of a system who can certainly be expected to have some interest in obtaining a useful response, even at the cost of having to intervene from time to time. As a consequence it seems rational to ask her for help whenever the system gets into trouble. The paradigm of programming by demonstration (pbd) provides a feasible framework for the particular kind of dialog required in such situations in which both user and agent use their individual capabilities not only to complement each other in order to overcome the current problem; instead the objective is to extend the agent's skills, thus enabling him to successfully deal with a wholeclass of problems and avoiding similar difficulties— and thus, additional training effort—for the future. 1

Programming by example: programming by demonstration for information agents

With the advent of e-commerce, systems supporting the user in finding just the right product in an electronic catalog have gained inreasing attention. While collaborative recommender systems (RS) derive their suggestions from other users' opinions, structure-based systems assess a product according to how well its properties satisfy a user's preferences. This paper presents the MAUT Machine, a system implementing the basic machinery to be used by a structure-based RS to elicit and maintain complex user preference models and evaluate the entries of an electronic catalog according to their appropriateness for a given user or group of users.

Multivariate preference models and decision making with the MAUT machine

To date, product recommendation systems have mainly been looked at from a single-agent perspective, where only the interests of a single user are taken into account. We extend this scenario and consider the case where multiple users are planning a joint purchase, and therefore many (potentially conflicting) interests have to be considered.

In this paper we present an overview of a system that assists a group of users to reach a joint decision on an online catalogue purchase. This is done by acquiring individual user models and using these models to mediate a kind of group discussion with the goal to arrive at a compromise that is acceptable to all group members.

Group decision making through mediated discussions

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mathias Bauer

Author Tools

Chat about Author