Search Results for author:
Found 18 papers, 4 papers with code
Self-Agreement: A Framework for Fine-tuning Language Models to Find Agreement among Diverse Opinions
In this paper, we propose Self-Agreement, a novel framework for fine-tuning LLMs to autonomously find agreement using data generated by LLM itself.
Best-Answer Prediction in Q&A Sites Using User Information
We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features, such as the textual content or the relationships with other participants.
Theme Aspect Argumentation Model for Handling Fallacies
We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability.
Fairness based Multi-Preference Resource Allocation in Decentralised Open Markets
In this work, we focus on resource allocation in a decentralised open market.
Federated Learning Versus Classical Machine Learning: A Convergence Comparison
Despite significant convergence, this training involves several privacy threats on participants' data when shared with the central cloud server.
Relational Argumentation Semantics
In this paper, we propose a fresh perspective on argumentation semantics, to view them as a relational database.
A Robust Model for Trust Evaluation during Interactions between Agents in a Sociable Environment
Trust evaluation is an important topic in both research and applications in sociable environments.
Privacy Information Classification: A Hybrid Approach
A large amount of information has been published to online social networks every day.
Abstract Interpretation in Formal Argumentation: with a Galois Connection for Abstract Dialectical Frameworks and May-Must Argumentation (First Report)
In this context, there is a more recently proposed formalism of may-must argumentation (MMA) that enforces still local but more abstract labelling conditions.
Evaluating the Communication Efficiency in Federated Learning Algorithms
These amounts of data are suitable for training different learning models.
Numerical Abstract Persuasion Argumentation for Expressing Concurrent Multi-Agent Negotiations
A negotiation process by 2 agents e1 and e2 can be interleaved by another negotiation process between, say, e1 and e3.
Broadening Label-based Argumentation Semantics with May-Must Scales (May-Must Argumentation)
In this work, we contemplate a way of broadening it by accommodating may- and must- conditions for an argument to be accepted or rejected, as determined by the number(s) of rejected and accepted attacking arguments.
Formulating Manipulable Argumentation with Intra-/Inter-Agent Preferences
From marketing to politics, exploitation of incomplete information through selective communication of arguments is ubiquitous.
An Ontology to support automated negotiation
In this work we propose an ontology to support automated negotiation in multiagent systems.
Holonic Multiagent Simulation of Complex Adaptive Systems
We propose a holonic multiagent simulator that can simulate any complex urban environment.
Traffic Simulation in Urban Networks Using Stochastic Cell Transmission Model
One of its advantages is that it can represent the uncertainty of the traffic states and the changing travel demand and supply conditions.
Complex Multi-Issue Negotiation Using Utility Hyper-Graphs
We evaluate the model experimentally using parameterized nonlinear utility spaces, showing that it can handle a large family of complex utility spaces by finding optimal contracts, outperforming previous sampling-based approaches.
Approximating Constraint-Based Utility Spaces Using Generalized Gaussian Mixture Models
Additionally, it leads us to a potential parametric model that could be used for opponent modeling in complex non-linear negotiations.
