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Damith C. Ranasinghe

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

Area-Optimal Control Strategies for Heterogeneous Multi-Agent Pursuit

Kamal Mammadov
Damith C. Ranasinghe

This paper presents a novel strategy for a multi-agent pursuit-evasion game involving multiple faster pursuers with heterogenous speeds and a single slower evader. We define a geometric region, the evader's safe-reachable set, as the intersection of Apollonius circles derived from each pursuer-evader pair. The capture strategy is formulated as a zero-sum game where the pursuers cooperatively minimize the area of this set, while the evader seeks to maximize it, effectively playing a game of spatial containment. By deriving the analytical gradients of the safe-reachable set's area with respect to agent positions, we obtain closed-form, instantaneous optimal control laws for the heading of each agent. These strategies are computationally efficient, allowing for real-time implementation. Simulations demonstrate that the gradient-based controls effectively steer the pursuers to systematically shrink the evader’s safe region, leading to guaranteed capture. This area-minimization approach provides a clear geometric objective for cooperative capture.

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AAAI Conference 2026 Conference Paper

Certified but Fooled! Breaking Certified Defenses with Ghost Certificates

Quoc Viet Vo
Tashreque Mohammed Haq
Paul Montague
Tamas Abraham
Ehsan Abbasnejad
Damith C. Ranasinghe

Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better understand the limits of guarantee provisions. Now, the objective is to not only mislead a classifier, but also to manipulate the certification process to generate a robustness guarantee for an adversarial input—certificate spoofing. A recent study in ICLR demonstrated that crafting large perturbations can shift inputs far into regions capable of generating a certificate for an incorrect class. Our study investigates if perturbations are needed to cause a misclassification and yet coax a certified model into issuing a deceptive, large robustness radius for a target class can still be made small and imperceptible. We explore the idea of region-focused adversarial examples to craft imperceptible perturbations, spoof certificates and achieve certification radii larger than the source class—ghost certificates. Extensive evaluations with the ImageNet demonstrate the ability to effectively bypass state-of-the-art certified defenses such as Densepure. Our work underscores the need to better understand the limits of robustness certification methods.

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AAMAS Conference 2026 Conference Paper

When LLM Agent Teams Fail at Topological Spatial Reasoning Under Partial Observability

Heitor Gama
Jean-Philippe Diguet
Damith C. Ranasinghe

Multi-agent planning and coordination remain challenging in partially observed environments. Large language models (LLMs) offer a solution by enabling text-native agents capable of planning and communicating in natural language. In this study, we examine a failure-prone ingredient for scalable coordination. In particular, we examine LLM-agents’ planning capabilities over map-like topologies, commonly encountered in navigation tasks. We attempt to stress topological spatial reasoning under decentralized information to assess their limitations. Through a task formulation, we isolate and study spatial reasoning failures hindering scalable coordination and share our findings to help advance methods to improve topological spatial reasoning. Supplementary material at https: //doi. org/10. 5281/zenodo. 18694368

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AAAI Conference 2025 Conference Paper

Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks

Bao Gia Doan
Afshar Shamsi
Xiao-Yu Guo
Arash Mohammadi
Hamid Alinejad-Rokny
Dino Sejdinovic
Damien Teney
Damith C. Ranasinghe

Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks, despite demonstrations of significant merits such as improved robustness and resilience to unseen or out-of-distribution inputs over their non-Bayesian counterparts. Although, Deep ensemble methods (Seligmann et al. 2024; Lakshminarayanan, Pritzel, and Blundell 2017) have proven to be highly effective for Bayesian deep learning, their practical application is hindered by substantial computational cost. In this study, we introduce an innovative framework to mitigate the computational burden of ensemble Bayesian deep learning. We explore a more feasible alternative, inspired by the recent success of low-rank adapters, we introduce Bayesian Low-Rank LeArning (Bella). We show, i) Bella achieves a dramatic reduction in the number of trainable parameters required to approximate a Bayesian posterior; and ii) it not only maintains, but in some instances, surpasses the performance–in accuracy and out-of-distribution generalisation–of conventional Bayesian learning methods and non-Bayesian baselines. Our extensive empirical evaluation in large-scale tasks such as ImageNet, CAMELYON17, DomainNet, VQA with CLIP, LLaVA demonstrate the effectiveness and versatility of Bella in building highly scalable and practical Bayesian deep models for real-world applications.

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AAAI Conference 2020 Conference Paper

Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects

Hoa Van Nguyen
Hamid Rezatofighi
Ba-Ngu Vo
Damith C. Ranasinghe

We consider the challenging problem of online planning for a team of agents to autonomously search and track a timevarying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conﬂicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking ﬁlter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1 − 1/e) approximation for maximizing the submodular multi-objective function.

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IJCAI Conference 2019 Conference Paper

SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks

Alireza Abedin
S. Hamid Rezatofighi
Qinfeng Shi
Damith C. Ranasinghe

Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited. In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data. We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces.

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JBHI Journal 2017 Journal Article

Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Asanga Wickramasinghe
Damith C. Ranasinghe
Christophe Fumeaux
Keith D. Hill
Renuka Visvanathan

Getting out of bed and ambulating without supervision is identified as one of the major causes of patient falls in hospitals and nursing homes. Therefore, increased supervision is proposed as a key strategy toward falls prevention. An emerging generation of batteryless, lightweight, and wearable sensors are creating new possibilities for ambulatory monitoring, where the unobtrusive nature of such sensors makes them particularly adapted for monitoring older people. In this study, we investigate the use of a batteryless radio-frequency identification (RFID) tag response to analyze bed-egress movements. We propose a bed-egress movement detection framework that includes a novel sequence learning classifier with a set of features derived from bed-egress motion analysis. We analyzed data from 14 healthy older people (66-86 years old) who wore a wearable embodiment of a batteryless accelerometer integrated RFID sensor platform loosely attached over their clothes at sternum level, and undertook a series of activities including bed-egress in two clinical room settings. The promising results indicate the efficacy of our batteryless bed-egress monitoring framework.

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