The adoption of immersive virtual reality (IVR) for gamified rehabilitation is increasing. However, a significant challenge relates to perceptions of virtual environments as empty and isolating, potentially increasing stress, particularly among older users (patients). This paper explores the use of a virtual peer mentor (VPM) in a custom IVR-rehabilitation game to provide guidance and companionship. This game specifically targets patients recovering from acute myocardial infarction (AMI), commonly known as a heart attack. Grounded in social support theory, the VPM provides three types of support: (1) informational support, through pre-exercise narratives detailing a shared medical history; (2) instrumental support, through real-time demonstrations of clinically-validated exercise movements; and (3) emotional support, through positive feedback and encouragement. A within-subjects study involving 30 hospitalized AMI patients (all over 47 years old) evaluated the effectiveness of the VPM-integrated IVR-rehabilitation game. Each participant experienced a baseline (no VPM) and VPM-integrated version of the game on separate days. The results from the Intrinsic Motivation Inventory (IMI) and the social presence module of the Game Experience Questionnaire (GEQ-SPM) show that the VPM resulted in significant increases in engagement, and statistically significant lower pressure/tension. Furthermore, participants exhibited high user acceptance (76.7%) and task-completion rates (98.5%), with minimal cybersickness. The findings demonstrate that a psychologically-grounded VPM can effectively reduce stress in middle- and older-age patients in an IVR rehabilitation setting.

Immersive Virtual Reality (IVR) exergaming presents a promising avenue to integrate physical exercise with engaging virtual experiences, potentially encouraging sustained physical activity. However, maintaining user motivation over extended periods poses a significant challenge. Recent research has introduced the Dynamic Difficulty Adjustment (DDA) mechanism, dynamically regulating exergame difficulty based on specific conditions to enhance user adaptation. While prior studies have predominantly focused on gaming performance to adjust difficulty, they often overlook the emotional impact on user motivation. This study investigates users' emotional responses to the game timer change (TC) as a DDA mechanism during IVR exergaming. Results indicate that subjects in the TC-implemented game displayed more neutral emotions, concomitant with improved gaming performance. Conversely, subjects in the game without TC exhibited a broader range of detected emotions (sad and happy), suggesting difficulties in adapting to in-game difficulty levels incongruent with their gaming abilities. Overall, this study establishes a foundation for future research in affective computing-based IVR exergaming, aiming to develop an intelligent autonomous DDA mechanism tailored to users' physical and mental conditions.

With the evolution of Internet of Things (IoT), some IoT nodes possess a certain degree of mobility, and the gains of the corresponding channels vary dramatically, incurring the energy supply problem for IoT nodes. To tackle this problem, we study a wireless-powered IoT (WPIoT), where a static$U$-antenna hybrid access point (HAP) coordinates the wireless energy transfer to mobile single-antenna IoT nodes and receives data from these IoT nodes. When IoT nodes have sufficient energy for transmitting generated data packets, we propose a generated data packets-based throughput maximization (GDPTM) algorithm for the short-term throughput maximization, and the GDPTM algorithm is designed to save nodes’ energy while transmitting all the generated data packets. Through monotonicity analysis, we prove the existence of the optimal transmit power that maximizes the throughput. When IoT nodes do not have sufficient energy for transmitting generated data packets, we propose a deep deterministic policy gradient (DDPG)-based multinode resource allocation (DMRA) algorithm. Through designing the action space, we find that the HAP under the DMRA algorithm manages the time, transmit power, and channel allocation of IoT nodes to improve the throughput. Numerical results validate that, when IoT nodes have sufficient energy, the GDPTM algorithm saves nodes’ energy and improves the throughput. When IoT nodes do not have sufficient energy, the DMRA algorithm also improves the throughput.

In many classification models, data is discretized to better estimate its distribution. Existing discretization methods often target at maximizing the discriminant power of discretized data, while overlooking the fact that the primary target of data discretization in classification is to improve the generalization performance. As a result, the data tend to be over-split into many small bins since the data without discretization retain the maximal discriminant information. Thus, we propose a Max-Dependency-Min-Divergence (MDmD) criterion that maximizes both the discriminant information and generalization ability of the discretized data. More specifically, the Max-Dependency criterion maximizes the statistical dependency between the discretized data and the classification variable while the Min-Divergence criterion explicitly minimizes the JS-divergence between the training data and the validation data for a given discretization scheme. The proposed MDmD criterion is technically appealing, but it is difficult to reliably estimate the high-order joint distributions of attributes and the classification variable. We hence further propose a more practical solution, Max-Relevance-Min-Divergence (MRmD) discretization scheme, where each attribute is discretized separately, by simultaneously maximizing the discriminant information and the generalization ability of the discretized data. The proposed MRmD is compared with the state-of-the-art discretization algorithms under the naive Bayes classification framework on 45 benchmark datasets. It significantly outperforms all the compared methods on most of the datasets.

For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.

The detail of the function PEO(.) in Section IV-B for this article was not available at the time of publication. It appears in Section IV-B as follows.

Recent work in multi-agent intention scheduling has shown that enabling agents to predict the actions of other agents when choosing their own actions can be beneficial. However existing approaches to 'intention-aware' scheduling assume that the programs of other agents are known, or are "similar" to that of the agent making the prediction. While this assumption is reasonable in some circumstances, it is less plausible when the agents are not co-designed. In this paper, we present a new approach to multi-agent intention scheduling in which agents predict the actions of other agents based on a high-level specification of the tasks performed by an agent in the form of a reward machine (RM) rather than on its (assumed) program. We show how a reward machine can be used to generate tree and rollout policies for an MCTS-based scheduler. We evaluate our approach in a range of multi-agent environments, and show that RM-based scheduling out-performs previous intention-aware scheduling approaches in settings where agents are not co-designed

Backscatter communications have been widely adopted in wireless networks for low-power IoT devices. For the devices which are powered by a battery or harvest energy from ambient signals, it is important to backscatter data in an energy-efficient manner. Inspired by the energy consumption disparity (ECD) between backscattering bit 0 and bit 1, we propose an energy-efficient multicodebook-based backscatter communication (MBBC) scheme, where multiple prefix codebooks, differentiated by multiple data rates, are meticulously designed and shared by the sender and the receiver. The sender backscatters the codewords in the corresponding codebooks, and the receiver recovers the original data by searching the corresponding codebooks. To design the energy-efficient multiple codebooks, we formulate the optimization problem as the minimization of the energy consumption of backscattering data. To address the optimization problem, we employ a forest to represent the multiple codebooks, where each codebook is represented by a binary tree. By conducting the pruning and expanding operations (PEOs) on the forest, we propose a heuristic algorithm to search the energy-efficient codebooks. Simulation results demonstrate that, compared with the other schemes, the proposed MBBC scheme significantly saves energy without sacrificing throughput.

We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. That is, while their goals are known, the precise programs used to achieve these goals are not known. In our approach, agents use an abstraction of their own program called a partially-ordered goal-plan tree (pGPT) to schedule their intentions and predict the actions of other agents. We show how a pGPT can be derived from the program of a BDI agent, and present an approach based on Monte Carlo Tree Search (MCTS) for scheduling an agent's intentions using pGPTs. We evaluate our pGPT-based approach in cooperative, selfish and adversarial multi-agent settings, and show that it out-performs MCTS-based scheduling where agents assume that other agents have the same program as themselves.

A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Previous approaches to the intention progression problem assume the agent has perfect information about the state of the environment. However, in many real-world applications, an agent may be uncertain about whether an environment condition holds, and hence whether a particular plan is applicable or an action is executable. In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain. We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent’s beliefs. The results suggest that SAU is able to successfully achieve the agent’s goals even in settings where there is significant uncertainty in the agent’s beliefs.

A key advantage the reactive planning approach adopted by BDI-based agents is the ability to recover from plan execution failures, and almost all BDI agent programming languages and platforms provide some form of failure handling mechanism. In general, these consist of simply choosing an alternative plan for the failed subgoal (e.g., JACK, Jadex). In this paper, we propose an alternative approach to recovering from execution failures that relies on exploiting positive interactions between an agent's intentions. A positive interaction occurs when the execution of an action in one intention assists the execution of actions in other intentions (e.g., by (re)establishing their preconditions). We have implemented our approach in a scheduling algorithm for BDI agents which we call SP. The results of a preliminary empirical evaluation of SP suggest our approach out-performs existing failure handling mechanisms used by state-of-the-art BDI languages. Moreover, the computational overhead of SP is modest.

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