Modeling Natural Language Sentences into SPN Graphs

Mills, M.T.; Psarologou, Adamantia; Bourbakis, Nikolaos G.

doi:10.1109/ictai.2013.135

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…For example, [58] describes a mapping mechanism of natural language sentences onto an SPN state machine for understanding purposes. In another example, [59] describes a procedure of modelling natural language sentences into SPN Graphs, while [60] describes a Natural Language Understanding -based method for a first level automatic categorisation of AI-based security documents.…”

Section: Artificial Intelligence and User Modelling In Service Of Use...mentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

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Section: Artificial Intelligence and User Modelling In Service Of Use...mentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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“…An important characteristic of a knowledge graph, in addition to representing a concept, is that it provides inference on the relations. The construction of knowledge graph starts with the extraction of information from text (Mills et al, 2013). The result is a list of concepts, labelled points, and a list of typed links between the points.…”

Section: Knowledge Representation Frameworkmentioning

confidence: 99%

“…Figure 6a illustrates a SPN knowledge graph (Mills et al, 2013). Figure 6b shows a similar SPN scheme using the agent→action→ patient kernel.…”

Section: The Spn Graph As a Mechanism For Representing Knowledgementioning

confidence: 99%

Demos

Bourbakis

Tsoukalas

Alamaniotis

et al. 2014

International Journal of Monitoring and Surveillance Technologies Research

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In today's dynamic network environments like smart cities, information evolves in distributed resources and in different forms or modalities. An agent or a system that wants to be actively part of this evolution has to be interactive, adaptive, autonomous and intelligent. The paper presents a new version of a distributed model-framework (called Demos) based on autonomous, intelligent agents with anticipatory responses. The first version of the system was proposed by , and the new features (LG Graphs, SPNs and NNs) were embedded in the version presented here. The Demos model is mainly based on the development of an adaptive distributed knowledge base system. Knowledge is represented in a form of frames with internal stochastic Petri-net graph for local representations (KR). A major advantage for the Demos model having a distributed (possibly across the net) is the adaptive knowledge base. Here the authors present the design of an adaptive knowledge model and its challenge, which lays principally on how to learn new knowledge by synthesising SPN forms, and how to develop anticipatory responses at an agent's site. The development of the Demos prototype has a great range of applicability, such as in autonomous negotiating teams, autonomous distributed units for energy efficient distribution, autonomous multiple mobile robots space exploration and maps generation, autonomous intelligent information agents (WWW), automatic information synthesis and fusion, etc. Here the authors have used this model for management of the electric power on a grid of a smart city.

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