4 citations · 7 across the 14 of their papers we have counts for
19 papers
Learning to adapt GR(1) specifications through degradation
Tiberiu-Andrei Georgescu, Dalal Alrajeh, Sebastian Uchitel
Reactive synthesis is a powerful tool for generating correct-by-construction controllers from formal specifications. GR(1) is an assume-guarantee specification framework that enabl…
A Model for Mediating Multi-Modal Human Intent into Safe Maneuvers for UAVs
Sofia Nelson, Dalal Alrajeh, Pedro Antonio Alarcon Granadeno +1
Direct human interaction with autonomous UAV systems can be enabled through modalities such as speech, gestures, and graphical interfaces. However, interpreting such inputs as dire…
How Analysts Use AI in High-Stakes Crime Linkage: An Industrial Study
Jessica Woodhams, Amy Burrell, Wanyin Li +9
Crime linkage analysis is used in many countries to identify series of offences that may have been committed by the same individual. In practice, specialist analysts manually searc…
Length Generalization with Log-Depth Recurrent Units
Charles Pert, Dalal Alrajeh, Alessandra Russo
Length generalization remains a persistent challenge for neural networks: recurrent models tend to suffer from positional biases, while transformers are constrained by fixed comput…
Data-Dependent Goal Modeling for ML-Enabled Law Enforcement Systems
Dalal Alrajeh, Vesna Nowack, Patrick Benjamin +15
Investigating serious crimes is inherently complex and resource-constrained. Law enforcement agencies (LEAs) grapple with overwhelming volumes of offender and incident data, making…
Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network
Yicheng Zhan, Fahim Ahmed, Amy Burrell +4
Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address limitations of traditional crime linkage methods in handling hi…
Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning
Tiberiu-Andrei Georgescu, Alexander W. Goodall, Dalal Alrajeh +2
Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specifications. Classical shielding approac…
RNN Generalization to Omega-Regular Languages
Charles Pert, Dalal Alrajeh, Alessandra Russo
Büchi automata (BAs) recognize -regular languages defined by formal specifications like linear temporal logic (LTL) and are commonly used in the verification of reactive systems…
Towards User-Centred Design of AI-Assisted Decision-Making in Law Enforcement
Vesna Nowack, Dalal Alrajeh, Carolina Gutierrez Muñoz +7
Artificial Intelligence (AI) has become an important part of our everyday lives, yet user requirements for designing AI-assisted systems in law enforcement remain unclear. To addre…
Towards Achieving Energy Efficiency and Service Availability in O-RAN via Formal Verification
Roberto Metere, Kangfeng Ye, Yue Gu +4
As Open Radio Access Networks (O-RAN) continue to expand, AI-driven applications (xApps) are increasingly being deployed enhance network management. However, developing xApps witho…
-regular Expression Synthesis from Transition-Based Büchi Automata
Charles Pert, Dalal Alrajeh, Alessandra Russo
A popular method for modelling reactive systems is to use -regular languages. These languages can be represented as nondeterministic Büchi automata (NBAs) or -regular express…
RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19 Assessment in Primary Care
Rakhilya Lee Mekhtieva, Brandon Forbes, Dalal Alrajeh +2
Clinical decision-making is a fundamental stage in delivering appropriate care to patients. In recent years several decision-making systems designed to aid the clinician in this pr…
Combining Experts' Causal Judgments
Dalal Alrajeh, Hana Chockler, Joseph Y. Halpern
Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts…
Learning Neural Search Policies for Classical Planning
Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo +1
Heuristic forward search is currently the dominant paradigm in classical planning. Forward search algorithms typically rely on a single, relatively simple variation of best-first s…
Minimal Assumptions Refinement for GR(1) Specifications
Davide G. Cavezza, Dalal Alrajeh, Andras Gyorgy
Reactive synthesis is concerned with finding a correct-by-construction controller from formal specifications, typically expressed in Linear Temporal Logic (LTL). The specifications…
Learning Classical Planning Strategies with Policy Gradient
Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo
A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search proces…
A Weakness Measure for GR(1) Formulae
Davide G. Cavezza, Dalal Alrajeh, András György
In spite of the theoretical and algorithmic developments for system synthesis in recent years, little effort has been dedicated to quantifying the quality of the specifications use…
Towards learning domain-independent planning heuristics
Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo +1
Automated planning remains one of the most general paradigms in Artificial Intelligence, providing means of solving problems coming from a wide variety of domains. One of the key f…
Interpolation-Based GR(1) Assumptions Refinement
Davide G. Cavezza, Dalal Alrajeh
This paper considers the problem of assumptions refinement in the context of unrealizable specifications for reactive systems. We propose a new counterstrategy-guided synthesis app…