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Gradual semantics have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper, we provide a novel methodology for obtaining gradual semantics for structured argumentation frameworks, where the building blocks of arguments and relations between them are known, unlike in QBAFs, where arguments are abstract entities. Differently from existing approaches, our methodology accommodates incomplete information about arguments' premises. We demonstrate the potential of our approach by introducing two different instantiations of the methodology, leveraging existing gradual semantics for QBAFs in these more complex frameworks. We also define a set of novel properties for gradual semantics in structured argumentation, discuss their suitability over a set of existing properties. Finally, we provide a comprehensive theoretical analysis assessing the instantiations, demonstrating the their advantages over existing gradual semantics for QBAFs and structured argumentation.

Criminal courts across the United States handle millions of cases every year, and the scheduling of those cases must accommodate a diverse set of constraints, including the preferences and availability of courts, prosecutors, and defense teams. When criminal court schedules are formed, defendants' scheduling preferences often take the least priority, although defendants may face significant consequences (including arrest or detention) for missed court dates. Additionally, studies indicate that defendants' nonappearances impose costs on the courts and other system stakeholders. To address these issues, courts and commentators have begun to recognize that pretrial outcomes for defendants and for the system would be improved with greater attention to court processes, including \emph{court scheduling practices}. There is thus a need for fair criminal court pretrial scheduling systems that account for defendants' preferences and availability, but the collection of such data poses logistical challenges. Furthermore, optimizing schedules fairly across various parties' preferences is a complex optimization problem, even when such data is available. In an effort to construct such a fair scheduling system under data uncertainty, this paper proposes a joint optimization and learning framework that combines machine learning models trained end-to-end with efficient matching algorithms. This framework aims to produce court scheduling schedules that optimize a principled measure of fairness, balancing the availability and preferences of all parties.

Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding and generation. Advanced utilization of the knowledge embedded in LLMs for automated annotation has consistently been explored. This study proposed to develop an emotion lexicon for Cantonese, a low-resource language, through collaborative efforts between LLM and human annotators. By integrating emotion labels provided by LLM and human annotators, the study leveraged existing linguistic resources including lexicons in other languages and local forums to construct a Cantonese emotion lexicon enriched with colloquial expressions. The consistency of the proposed emotion lexicon in emotion extraction was assessed through modification and utilization of three distinct emotion text datasets. This study not only validates the efficacy of the constructed lexicon but also emphasizes that collaborative annotation between human and artificial intelligence can significantly enhance the quality of emotion labels, highlighting the potential of such partnerships in facilitating natural language processing tasks for low-resource languages., Comment: 13 pages

This paper analyses the security performance of a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication system with integrated sensing and communications (ISAC). We consider a multiple-antenna UAV transmitting ISAC waveforms to simultaneously detect an untrusted target in the surrounding environment and communicate with a ground Internet-of-Things (IoT) device in the presence of an eavesdropper (Eve). Given that the Eve can conceal their channel state information (CSI) in practical scenarios, we assume that the CSI of the eavesdropper channel is imperfect. For this RIS-aided ISAC-UAV system, we aim to maximize the average communication secrecy rate by jointly optimizing UAV trajectory, RIS passive beamforming, transmit beamforming, and receive beamforming. However, this joint optimization problem is non-convex due to multi-variable coupling. As such, we solve the optimization using an efficient and tractable algorithm using a block coordinate descent (BCD) method. Specifically, we develop a successive convex approximation (SCA) algorithm based on semidefinite relaxation (SDR) to optimise the joint optimization as four separate non-convex subproblems. Numerical results show that our proposed algorithm can successfully ensure the accuracy of sensing targets and significantly improve the communication secrecy rate of the IoT communication devices.

In mobile systems with low-altitude vehicles, integrated sensing and communication (ISAC) is considered an effective approach to increase the transmission rate due to limited spectrum resources. To further improve the ISAC performance, this paper proposes a novel method called integrated sensing and communication-movable antenna (ISAC-MA) to optimize the antenna's position. Our goal is to support low-space vehicles by optimizing radar and communication joint beamforming and antenna position in the presence of clutter. This scheme not only guarantees the required signal-to-noise ratio (SNR) for sensing but also further improves the SNR for communication. A successive convex approximation (SCA)-based block coordinate descent (BCD) algorithm is proposed to maximize communication capacity under the condition of sensing SNR. Numerical results show that, compared with the traditional ISAC system and various benchmark schemes, the proposed ISAC-MA system can achieve higher communication capacity under the same sensing SNR constraints.

This paper investigates a novel unmanned aerial vehicle (UAV) secure communication system with integrated sensing and communications. We consider wireless security enhancement for a multiple-antenna UAV transmitting ISAC waveforms to communicate with multiple ground Internet-of-Thing devices and detect the surrounding environment. Specifically, we aim to maximize the average communication secrecy rate by optimizing the UAV trajectory and beamforming vectors. Given that the UAV trajectory optimization problem is non-convex due to multi-variable coupling develop an efficient algorithm based on the successive convex approximation (SCA) algorithm. Numerical results show that our proposed algorithm can ensure the accuracy of sensing targets and improve the communication secrecy rate.

We present TRACE-cs, a novel hybrid system that combines symbolic reasoning with large language models (LLMs) to address contrastive queries in scheduling problems. TRACE-cs leverages SAT solving techniques to encode scheduling constraints and generate explanations for user queries, while utilizing an LLM to process the user queries into logical clauses as well as refine the explanations generated by the symbolic solver to natural language sentences. By integrating these components, our approach demonstrates the potential of combining symbolic methods with LLMs to create explainable AI agents with correctness guarantees.

To enable effective human-AI collaboration, merely optimizing AI performance while ignoring humans is not sufficient. Recent research has demonstrated that designing AI agents to account for human behavior leads to improved performance in human-AI collaboration. However, a limitation of most existing approaches is their assumption that human behavior is static, irrespective of AI behavior. In reality, humans may adjust their action plans based on their observations of AI behavior. In this paper, we address this limitation by enabling a collaborative AI agent to consider the beliefs of its human partner, i.e., what the human partner thinks the AI agent is doing, and design its action plan to facilitate easier collaboration with its human partner. Specifically, we developed a model of human beliefs that accounts for how humans reason about the behavior of their AI partners. Based on this belief model, we then developed an AI agent that considers both human behavior and human beliefs in devising its strategy for working with humans. Through extensive real-world human-subject experiments, we demonstrated that our belief model more accurately predicts humans' beliefs about AI behavior. Moreover, we showed that our design of AI agents that accounts for human beliefs enhances performance in human-AI collaboration.

In H-mode tokamak plasmas, the plasma is sometimes ejected beyond the edge transport barrier. These events are known as edge localized modes (ELMs). ELMs cause a loss of energy and damage the vessel walls. Understanding the physics of ELMs and by extension, how to detect and mitigate them, is an important challenge. In this paper, we focus on two diagnostic methods $\unicode{x2013}$ D-alpha spectroscopy and Doppler backscattering (DBS). The former detects ELMs by measuring Balmer alpha emission while the latter uses microwave radiation to probe the plasma. DBS has the advantage of having higher temporal resolution and robustness to damage. These advantages of DBS diagnostics may be beneficial for future operational tokamaks and thus data processing techniques for DBS should be developed in preparation. In sight of this, we explore the training of neural networks to detect ELMs from DBS data, using D-alpha data as the ground truth. With shots found in the DIII-D database, the model is trained to classify each time step based on the occurrence of an ELM event. The results are promising. When tested on shots similar to those used for training, the model is capable of consistently achieving a high f1-score of 0.93. This score is a performance metric for imbalanced datasets that ranges between 0 and 1. We evaluate the performance of our neural network on a variety of ELMs $\unicode{x2013}$ grasssy, suppressed, and wide pedestal $\unicode{x2013}$ finding broad applicability. Beyond ELMs, our work demonstrates the wider feasibility of applying neural networks to data from DBS diagnostics.

Traditional belief revision frameworks often rely on the principle of minimalism, which advocates minimal changes to existing beliefs. However, research in human cognition suggests that people are inherently driven to seek explanations for inconsistencies, thereby striving for explanatory understanding rather than minimal changes when revising beliefs. Traditional frameworks often fail to account for these cognitive patterns, relying instead on formal principles that may not reflect actual human reasoning. To address this gap, we introduce Human-Aware Belief Revision, a cognitively-inspired framework for modeling human belief revision dynamics, where given a human model and an explanation for an explanandum, revises the model in a non-minimal way that aligns with human cognition. Finally, we conduct two human-subject studies to empirically evaluate our framework under real-world scenarios. Our findings support our hypotheses and provide insights into the strategies people employ when resolving inconsistencies, offering some guidance for developing more effective human-aware AI systems.

Explanation generation frameworks aim to make AI systems' decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this paper, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations. Monolithic explanations provide self-contained reasons for an explanandum without considering the agent receiving the explanation, while model reconciling explanations account for the knowledge of the agent receiving the explanation. For monolithic explanations, our approach integrates uncertainty by utilizing probabilistic logic to increase the probability of the explanandum. For model reconciling explanations, we propose a framework that extends the logic-based variant of the model reconciliation problem to account for probabilistic human models, where the goal is to find explanations that increase the probability of the explanandum while minimizing conflicts between the explanation and the probabilistic human model. We introduce explanatory gain and explanatory power as quantitative metrics to assess the quality of these explanations. Further, we present algorithms that exploit the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Extensive experimental evaluations on various benchmarks demonstrate the effectiveness and scalability of our approach in generating explanations under uncertainty.

Explainable AI Planning (XAIP) aims to develop AI agents that can effectively explain their decisions and actions to human users, fostering trust and facilitating human-AI collaboration. A key challenge in XAIP is model reconciliation, which seeks to align the mental models of AI agents and humans. While existing approaches often assume a known and deterministic human model, this simplification may not capture the complexities and uncertainties of real-world interactions. In this paper, we propose a novel framework that enables AI agents to learn and update a probabilistic human model through argumentation-based dialogues. Our approach incorporates trust-based and certainty-based update mechanisms, allowing the agent to refine its understanding of the human's mental state based on the human's expressed trust in the agent's arguments and certainty in their own arguments. We employ a probability weighting function inspired by prospect theory to capture the relationship between trust and perceived probability, and use a Bayesian approach to update the agent's probability distribution over possible human models. We conduct a human-subject study to empirically evaluate the effectiveness of our approach in an argumentation scenario, demonstrating its ability to capture the dynamics of human belief formation and adaptation.

In today's competitive financial landscape, understanding and anticipating customer goals is crucial for institutions to deliver a personalized and optimized user experience. This has given rise to the problem of accurately predicting customer goals and actions. Focusing on that problem, we use historical customer traces generated by a realistic simulator and present two simple models for predicting customer goals and future actions -- an LSTM model and an LSTM model enhanced with state-space graph embeddings. Our results demonstrate the effectiveness of these models when it comes to predicting customer goals and actions., Comment: Accepted at the FinPlan 2023 workshop at ICAPS 2023

Goal recognition design aims to make limited modifications to decision-making environments with the goal of making it easier to infer the goals of agents acting within those environments. Although various research efforts have been made in goal recognition design, existing approaches are computationally demanding and often assume that agents are (near-)optimal in their decision-making. To address these limitations, we introduce a data-driven approach to goal recognition design that can account for agents with general behavioral models. Following existing literature, we use worst-case distinctiveness($\textit{wcd}$) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment. Our approach begins by training a machine learning model to predict the $\textit{wcd}$ for a given environment and the agent behavior model. We then propose a gradient-based optimization framework that accommodates various constraints to optimize decision-making environments for enhanced goal recognition. Through extensive simulations, we demonstrate that our approach outperforms existing methods in reducing $\textit{wcd}$ and enhancing runtime efficiency in conventional setup. Moreover, our approach also adapts to settings in which existing approaches do not apply, such as those involving flexible budget constraints, more complex environments, and suboptimal agent behavior. Finally, we have conducted human-subject experiments which confirm that our method can create environments that facilitate efficient goal recognition from real-world human decision-makers.

Introduction: The current study investigates the complex association between COVID-19 and the studied districts' socioecology (e.g. internal and external built environment, sociodemographic profiles, etc.) to quantify their contributions to the early outbreaks and epidemic resurgence of COVID-19. Methods: We aligned the analytic model's architecture with the hierarchical structure of the resident's socioecology using a multi-headed hierarchical convolutional neural network to structure the vast array of hierarchically related predictive features representing buildings' internal and external built environments and residents' sociodemographic profiles as model input. COVID-19 cases accumulated in buildings across three adjacent districts in HK, both before and during HK's epidemic resurgence, were modeled. A forward-chaining validation was performed to examine the model's performance in forecasting COVID-19 cases over the 3-, 7-, and 14-day horizons during the two months subsequent to when the model for COVID-19 resurgence was built to align with the forecasting needs in an evolving pandemic. Results: Different sets of factors were found to be linked to the earlier waves of COVID-19 outbreaks compared to the epidemic resurgence of the pandemic. Sociodemographic factors such as work hours, monthly household income, employment types, and the number of non-working adults or children in household populations were of high importance to the studied buildings' COVID-19 case counts during the early waves of COVID-19. Factors constituting one's internal built environment, such as the number of distinct households in the buildings, the number of distinct households per floor, and the number of floors, corridors, and lifts, had the greatest unique contributions to the building-level COVID-19 case counts during epidemic resurgence.

Emergency department's (ED) boarding (defined as ED waiting time greater than four hours) has been linked to poor patient outcomes and health system performance. Yet, effective forecasting models is rare before COVID-19, lacking during the peri-COVID era. Here, a hybrid convolutional neural network (CNN)-Long short-term memory (LSTM) model was applied to public-domain data sourced from Hong Kong's Hospital Authority, Department of Health, and Housing Authority. In addition, we sought to identify the phase of the COVID-19 pandemic that most significantly perturbed our complex adaptive healthcare system, thereby revealing a stable pattern of interconnectedness among its components, using deep transfer learning methodology. Our result shows that 1) the greatest proportion of days with ED boarding was found between waves four and five; 2) the best-performing model for forecasting ED boarding was observed between waves four and five, which was based on features representing time-invariant residential buildings' built environment and sociodemographic profiles and the historical time series of ED boarding and case counts, compared to during the waves when best-performing forecasting is based on time-series features alone; and 3) when the model built from the period between waves four and five was applied to data from other waves via deep transfer learning, the transferred model enhanced the performance of indigenous models.

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tampering attacks in NS networks, we propose a novel optimization framework for reliable NS resource allocation in a blockchain-secured low-latency wireless network, where trusted base stations (BSs) with high reputations are selected for blockchain management and NS service-provisioning. For such a blockchain-secured network, we consider that the latency is measured by the summation of blockchain management and NS service-provisioning, whilst the NS reliability is evaluated by the BS denial-of-service (DoS) probability. To satisfy the requirements of both the latency and reliability, we formulate a constrained computing resource allocation optimization problem to minimize the total processing latency subject to the BS DoS probability. To efficiently solve the optimization, we design a constrained deep reinforcement learning (DRL) algorithm, which satisfies both latency and DoS probability requirements by introducing an additional critic neural network. The proposed constrained DRL further solves the issue of high input dimension by incorporating feature engineering technology. Simulation results validate the effectiveness of our approach in achieving reliable and low-latency NS service-provisioning in the considered blockchain-secured wireless network., Comment: arXiv admin note: substantial text overlap with arXiv:2312.08016

In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN), with which the number of training parameters does not change with the number of users. To enable the generalization of the GNN, we develop a hybrid-task meta-learning (HML) algorithm that trains the initial parameters of the GNN with different communication scenarios during meta-training. Next, during meta-testing, a few samples are used to fine-tune the GNN with unseen communication scenarios. Simulation results demonstrate that our HML approach can improve the initial performance by $8.79\%$, and sampling efficiency by $73\%$, compared with existing benchmarks. After fine-tuning, our near-optimal GNN-based policy can achieve close to the same reward with much lower inference complexity compared to the optimal policy obtained using iterative optimization.

This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper. To improve the privacy and prevent eavesdropping attacks, we propose a user scheduling algorithm to schedule users satisfying an instantaneous minimum secrecy rate constraint. Based on this, we optimize the bandwidth allocations with three algorithms namely iterative search (IvS), GNN-based supervised learning (GNN-SL), and GNN-based unsupervised learning (GNN-USL). We present a computational complexity analysis which shows that GNN-SL and GNN-USL can be more efficient compared to IvS which is limited by the bandwidth block size. Numerical simulation results highlight that our proposed GNN-based resource allocations can achieve a comparable sum secrecy rate compared to IvS with significantly lower computational complexity. Furthermore, we observe that the GNN approach is more robust to uncertainties in the eavesdropper's channel state information, especially compared with the best channel allocation scheme.

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks.} {We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability}. {We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to} validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and {MEC} resource allocation algorithms.

This paper investigates the optimization of reconfigurable intelligent surface (RIS) in an integrated sensing and communication (ISAC) system. \red{To meet the demand of growing number of devices, power domain non-orthogonal multiple access (NOMA) is considered. However, traditional NOMA with a large number of devices is challenging due to large decoding delay and propagation error introduced by successive interference cancellation (SIC). Thus, OMA is integrated into NOMA to support more devices}. We formulate a max-min problem to optimize the sensing beampattern \red{with constraints on communication rate}, through joint power allocation, active beamforming and RIS phase shift design. To solve the non-convex problem with a non-smooth objective function, we propose a low complexity alternating optimization (AO) algorithm, where a closed form expression for the intra-cluster power allocation (intra-CPA) is derived, and penalty and successive convex approximation (SCA) methods are used to optimize the beamforming and phase shift design. Simulation results show the effectiveness of the proposed algorithm in terms of improving minimum beampattern gain (MBPG) compared with other baselines. Furthermore, the trade-off between sensing and communication is analyzed and demonstrated in the simulation results.

We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting a structured argumentation-based dialogue paradigm, our framework enables dialectical reconciliation to address knowledge discrepancies between an explainer (AI agent) and an explainee (human user), where the goal is for the explainee to understand the explainer's decision. We formally describe the operational semantics of our proposed framework, providing theoretical guarantees. We then evaluate the framework's efficacy ``in the wild'' via computational and human-subject experiments. Our findings suggest that our framework offers a promising direction for fostering effective human-AI interactions in domains where explainability is important.