Towards Self-Explaining Agents

Fähndrich, Johannes; Ahrndt, Sebastian; Albayrak, Şahin

doi:10.1007/978-3-319-00563-8_18

Cited by 7 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, agents need to be aware of their own and the other agents' association. A possible way to realize this is enhancing agents with self-descriptiveness as introduced in [156,157].…”

Section: Control Conflictsmentioning

confidence: 99%

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Schmidt

Åhlund

2018

Renewable and Sustainable Energy Reviews

107

View full text Add to dashboard Cite

Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stock's energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current state of the art of interrelated topics in data handling, building automation, distributed control, and semantics. The comprehensive overview leads to seven research questions guiding future research directions.

show abstract

“…However, agents need to be aware of their own and the other agents' association. A possible way to realize this is enhancing agents with self-descriptiveness as introduced in [156,157].…”

Section: Control Conflictsmentioning

confidence: 99%

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Schmidt

Åhlund

2018

Renewable and Sustainable Energy Reviews

107

View full text Add to dashboard Cite

show abstract

“…IA's SA is used to determine any necessary actions in response to given situations [4][5][6]. However, without a proper tool for humans to know IA's immediate future actions, it will cost human response time to go back to the driving loop control to take necessary actions over the car when the predicted IA's actions go south [7][8][9]. Level four of driving automation, namely partially automated driving (also called collaborative driving in this paper), according to the Society of Automotive Engineers is considered an example of HAT that can benefit from the immediate future action predictor for its autopilot agent [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

Kridalukmana,

Eridani,

Septiana

2024

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop anapproach to predict the immediate future actions of an intelligent agent with partially automated drivingas the experimental case. The proposed approach relies on a machine learning method called naive Bayesto develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. Theautonomous driving simulation software called Carla is used for the simulation. The results show that theproposed approach is applicable to predict an intelligent agent’s three-second time-window immediate futureaction.

show abstract

Self-explaining Digital Systems: Technical View, Implementation Aspects, and Completeness

Fey

Drechsler

2019

Advanced Boolean Techniques

View full text Add to dashboard Cite

Towards Self-Explaining Agents

Cited by 7 publications

References 15 publications

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

Self-explaining Digital Systems: Technical View, Implementation Aspects, and Completeness

Contact Info

Product

Resources

About