Search Results for author:
Found 34 papers, 8 papers with code
Approaches to human activity recognition via passive radar
The thesis explores novel methods for Human Activity Recognition (HAR) using passive radar with a focus on non-intrusive Wi-Fi Channel State Information (CSI) data.
CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning
We demonstrate two uses for these metrics: for learning, an agent that clusters MDPs based on a CHIRP metric achieves $17\%$ higher average returns than three existing agents in a sequence of MetaWorld tasks.
Learning Robust Reward Machines from Noisy Labels
This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces.
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i. e., events for which we have limited to no training data.
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer
This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking.
Speaking Your Language: Spatial Relationships in Interpretable Emergent Communication
The results indicate that agents can develop a language capable of expressing the relationships between parts of their observation, achieving over 90% accuracy when trained in a referential game which requires such communication.
Assessing the Robustness of Intelligence-Driven Reinforcement Learning
Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail.
Knowledge from Uncertainty in Evidential Deep Learning
In this paper, we empirically investigate the correlations between misclassification and evaluated uncertainty, and show that EDL's `evidential signal' is due to misclassification bias.
Sound-skwatter (Did You Mean: Sound-squatter?) AI-powered Generator for Phishing Prevention
Sound-squatting is a phishing attack that tricks users into malicious resources by exploiting similarities in the pronunciation of words.
It's About Time: Temporal References in Emergent Communication
Emergent communication studies the development of language between autonomous agents, aiming to improve understanding of natural language evolution and increase communication efficiency.
Research Note on Uncertain Probabilities and Abstract Argumentation
The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1. 5C (medium confidence)."
SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks
In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i. e., probabilities over probabilities.
Uncertain Bayesian Networks: Learning from Incomplete Data
When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated.
Using DeepProbLog to perform Complex Event Processing on an Audio Stream
We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.
Fudge: A light-weight solver for abstract argumentation based on SAT reductions
We present Fudge, an abstract argumentation solver that tightly integrates satisfiability solving technology to solve a series of abstract argumentation problems.
Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits
When collaborating with an AI system, we need to assess when to trust its recommendations.
An Experimentation Platform for Explainable Coalition Situational Understanding
We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing.
BIG-bench Machine Learning
Explainable artificial intelligence
Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams
Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the human users and expected to operate in a broad set of scenarios rather than being narrowly defined for particular purposes.
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
Uncertainty-Aware Deep Classifiers using Generative Models
Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution.
Increasing negotiation performance at the edge of the network
In particular, we show our method significantly reduces the number of messages when an agreement is not possible.
The current state of automated negotiation theory: a literature review
Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting.
Explainable AI for Intelligence Augmentation in Multi-Domain Operations
Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of remote and autonomous sensors, and human intelligence, distributed among multiple partners.
AFRA: Argumentation framework with recursive attacks
The issue of representing attacks to attacks in argumentation is receiving an increasing attention as a useful conceptual modelling tool in several contexts.
Automata for Infinite Argumentation Structures
The theory of abstract argumentation frameworks (afs) has, in the main, focused on finite structures, though there are many significant contexts where argumentation can be regarded as a process involving infinite objects.
Uncertainty Aware AI ML: Why and How
This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios.
Probabilistic Logic Programming with Beta-Distributed Random Variables
We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables.
On Natural Language Generation of Formal Argumentation
In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces.
Solving Set Optimization Problems by Cardinality Optimization via Weak Constraints with an Application to Argumentation
Optimization - minimization or maximization - in the lattice of subsets is a frequent operation in Artificial Intelligence tasks.
Exploiting Parallelism for Hard Problems in Abstract Argumentation: Technical Report
Abstract argumentation framework (\AFname) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation.
Reasoning about the Impacts of Information Sharing
In this paper we describe a decision process framework allowing an agent to decide what information it should reveal to its neighbours within a communication graph in order to maximise its utility.
Subjective Logic Operators in Trust Assessment: an Empirical Study
Computational trust mechanisms aim to produce trust ratings from both direct and indirect information about agents' behaviour.
Computing Preferred Extensions in Abstract Argumentation: a SAT-based Approach
This paper presents a novel SAT-based approach for the computation of extensions in abstract argumentation, with focus on preferred semantics, and an empirical evaluation of its performances.
