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Found 15 papers, 0 papers with code
Robustness of Decentralised Learning to Nodes and Data Disruption
Through these configurations, we are able to show the non-trivial interplay between the properties of the network connecting nodes, the persistence of knowledge acquired collectively before disruption or lack thereof, and the effect of data availability pre- and post-disruption.
Optimizing Risk-averse Human-AI Hybrid Teams
For hybrid teams, we will refer to both the humans and AI systems as agents.
Impact of network topology on the performance of Decentralized Federated Learning
We highlight the challenges in transferring knowledge from peripheral to central nodes, attributed to a dilution effect during model aggregation.
Optimizing Delegation in Collaborative Human-AI Hybrid Teams
The manager learns a model of behavior linking observations of agent performance and the environment/world the team is operating in, and from these observations makes the most desirable selection of a control agent.
Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios
Thus, fully decentralized learning can help in this case.
Optimizing delegation between human and AI collaborative agents
In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions.
Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems
In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent failure as a result of their sensing capabilities and possible deficiencies.
Modeling Human Behavior Part I -- Learning and Belief Approaches
To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them.
Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty
We will arrange these topics as follows (i) methods such as cognitive architectures, cognitive heuristics, and related which demonstrate assumptions of limitations on cognitive resources and how that impacts decisions and (ii) methods which generate and utilize representations of bias or uncertainty to model human decision-making or the future outcomes of decisions.
A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents
With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human.
Structural invariants and semantic fingerprints in the "ego network" of words
In this respect, ring #1 can be seen as the semantic fingerprint of the ego network of words.
A communication efficient distributed learning framework for smart environments
In the specific case analysed in the paper, we focus on a learning task, considering two distributed learning algorithms.
Energy efficient distributed analytics at the edge of the network for IoT environments
We analyse the performance of different configurations of the distributed learning framework, in terms of (i) accuracy obtained in the learning task and (ii) energy spent to send data between the involved nodes.
Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks
In order to validate this claim, we focus on popular feature-extraction prediction algorithms (both unsupervised and supervised) and we extend them to include social-circles awareness.
Optimising cost vs accuracy of decentralised analytics in fog computing environments
Decentralising AI tasks on several cooperative devices means identifying the optimal set of locations or Collection Points (CP for short) to use, in the continuum between full centralisation (i. e., all data on a single device) and full decentralisation (i. e., data on source locations).
