An Efficient Explainable Artificial Intelligence Model of Automatically Generated Summaries Evaluation: A Use Case of Bridging Cognitive Psychology and Computational Linguistics

2021 ◽  
pp. 69-90
Author(s):  
Alaidine Ben Ayed ◽  
Ismaïl Biskri ◽  
Jean-Guy Meunier
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Psychology ◽  
Computational Linguistics ◽  
Use Case ◽  
Explainable Artificial Intelligence

Story and discourse

2007 ◽  
Vol 8 (2) ◽  
pp. 177-208 ◽  
Author(s):  
R. Michael Young
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Psychology ◽  
Virtual Environment ◽  
Computational Linguistics ◽  
Virtual Worlds ◽  
Narrative Theory ◽  
Interactive Narrative ◽  
Basic Approach ◽  
Communicative Actions ◽  
Story And Discourse

In this paper, we set out a basic approach to the modeling of narrative in interactive virtual worlds. This approach adopts a bipartite model taken from narrative theory, in which narrative is composed of story and discourse. In our approach, story elements — plot and character — are defined in terms of plans that drive the dynamics of a virtual environment. Discourse elements — the narrative’s communicative actions — are defined in terms of discourse plans whose communicative goals include conveying the story world plan’s structure. To ground the model in computational terms, we provide examples from research under way in the Liquid Narrative Group involving the design of the Mimesis system, an architecture for intelligent interactive narrative incorporating concepts from artificial intelligence, narrative theory, cognitive psychology and computational linguistics.

Successful Use Case Applications of Artificial Intelligence in the Steel Industry

2019 ◽  
Author(s):  
L. Ometto ◽  
S. Challapalli ◽  
M. Polo ◽  
G. Cestari ◽  
A. Villagrossi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Steel Industry ◽  
Use Case

scholarly journals Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽  
2021 ◽  
Author(s):  
Steven Umbrello ◽  
Ibo van de Poel
Keyword(s):  
Artificial Intelligence ◽  
Contact Tracing ◽  
Use Case ◽  
Value Sensitive Design ◽  
Specific Design ◽  
Established Method ◽  
Social Good ◽  
Do No Harm ◽  
Design Requirements ◽  
Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

Principles of induction and approaches to attribute based induction

1991 ◽  
Vol 6 (4) ◽  
pp. 307-333 ◽  
Author(s):  
G. Kalkanis ◽  
G. V. Conroy
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Cognitive Psychology ◽  
Expert Systems ◽  
Basic Principles ◽  
Inductive Approach ◽  
Induction Process

AbstractThis paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.

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