Ladislau Bölöni

University of Central Florida | UCF · Department of Electrical Engineering & Computer Science

In this work, we propose a blockchain-based solution for securing robot-to-robot communication for a task with a high socioeconomic impact—information gathering. The objective of the robots is to gather maximal information about an unknown ambient phenomenon such as soil humidity distribution in a field. More specifically, we use the proof-of-work...

In today's era of automation, mobile robots are being used for collecting meaningful information about an ambient phenomenon such as temperature or moisture distribution in an agricultural field. Most of the studies in the literature assume that the underlying information field is Gaussian, and therefore, Gaussian Process (GP)-based models are extr...

Recent developments in robotic and sensor hardware make data collection with mobile robots (ground or aerial) feasible and affordable to a wide population of users. The newly emergent applications, such as precision agriculture, weather damage assessment, or personal home security often do not satisfy the simplifying assumptions made by previous re...

Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions. While both the available data and the sophistication of the AI models and available computing power exceed what was ava...

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while significantly mitigating cybersecurity risks. Tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory and computation) required by blockc...

Multi-robot teams are becoming an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, surveying the aftermath of natural disasters or tracking pollution. These robot teams are often assembled from untrusted devices not owned by the user, making the maintenance of the integri...

Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions. While both the available data and the sophistication of the AI models and available computing power exceed what was ava...

Precision agriculture is the collection of hardware and software technologies that allow a farmer to make informed, differentiated decisions regarding agricultural operations such as planting, fertilizing, pest control, and harvesting. In recent years, advances in agricultural machinery and the emergence of agricultural robots continuously increase...

In this paper we describe an intelligent taxi dispatch system that has the goal of reducing the waiting time of the passengers and the idle driving distance of the taxis. The system relies on two separate models that predict the probability distributions of the taxi demand and destinations respectively. The models are learned from historical data a...

In a coordinated multi-robot information sampling scenario, robots often share their collected information with others for a better prediction. As with any other online data sharing technique, data integrity is a concern, but it has not yet been addressed in the multi-robot information sampling literature. In this paper, we study how to secure the...

We study the problem of information sampling of an ambient phenomenon using a group of mobile robots. Autonomous robots are being deployed for various applications such as precision agriculture, search-and-rescue, among others. These robots are usually equipped with sensors and tasked with collecting maximal information for further data processing...

In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost,...

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while mitigating significant cybersecurity risks pertaining to the main application. Associated tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g....

In recent years, significant work has been done in technological enhancements for mobility aids (smart walkers). However, most of this work does not cover the millions of people who have both mobility and visual impairments. In this paper, we design and study four different configurations of smart walkers that are specifically targeted to the needs...

The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior. To speed up the learning, several researchers designed collaborative learning systems that use data from multiple users. However, disclosing the daily activities of a...

Delivering the right content at the right time is one of the main challenges in designing smart information delivery systems. Predicting the user’s preferences in the future and caching the required content in advance to improve the quality of service has been proposed and investigated before for different applications. In this paper, we explicitly...

The first deep RL algorithm, DQN, was limited by the overestimation bias of the learned Q-function. Subsequent algorithms proposed techniques to reduce this problem, without fully eliminating it. Recently, the Maxmin and Ensemble Q-learning algorithms used the different estimates provided by ensembles of learners to reduce the bias. Unfortunately,...

Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation. Unfortunately, clustering and augmentation are domain-dependent, and thus they require either manual tweaking or expensive learning. In this work, we describe an approach that generates meta-...

Many cooperative multi-agent problems require agents to learn individual tasks while contributing to the collective success of the group. This is a challenging task for current state-of-the-art multi-agent reinforcement algorithms that are designed to either maximize the global reward of the team or the individual local rewards. The problem is exac...

Augmented/virtual reality (AR/VR) technologies can be deployed in a household environment for applications such as checking the weather or traffic reports, watching a summary of news, or attending classes. Since AR/VR applications are highly delay sensitive, delivering these types of reports in maximum quality could be very challenging. In this pap...

Recent research demonstrated that it is feasible to end-to-end train multi-task deep visuomotor policies for robotic manipulation using variations of learning from demonstration (LfD) and reinforcement learning (RL). In this paper, we extend the capabilities of end-to-end LfD architectures to object manipulation in clutter. We start by introducing...

We explore a collaborative and cooperative multi-agent reinforcement learning setting where a team of reinforcement learning agents attempt to solve a single cooperative task in a multi-scenario setting. We propose a novel multi-agent reinforcement learning algorithm inspired by universal value function approximators that not only generalizes over...

We are considering a scenario where a team of bodyguard robots provides physical protection to a VIP in a crowded public space. We use deep reinforcement learning to learn the policy to be followed by the robots. As the robot bodyguards need to follow several difficult-to-reconcile goals, we study several primitive and composite reward functions an...

In this paper we are considering a scenario where a team of robot bodyguards are providing physical protection to a VIP in a crowded public space. We show that the problem involves a complex mesh of interactions between the VIP and the robots, between the robots themselves and the robots and the bystanders respectively. We show how recently propose...

Few-shot or one-shot learning of classifiers for images or videos is an important next frontier in computer vision. The extreme paucity of training data means that the learning must start with a significant inductive bias towards the type of task to be learned. One way to acquire this is by meta-learning on tasks similar to the target task. However...

Sensor nodes in underwater sensor networks may acquire data at a higher rate than their ability to communicate over underwater acoustic channels. Autonomous underwater vehicles may mitigate this mismatch by offloading high volumes of data from the sensor nodes and ferrying them to the sink. Such a mode of data transfer results in high latency. Occa...

Several recent projects demonstrated the promise of end-to-end learned deep visuomotor policies for robot manipulator control. Despite impressive progress, these systems are known to be vulnerable to physical disturbances, such as accidental or adversarial bumps that make them drop the manipulated object. They also tend to be distracted by visual d...

We are considering the problem of controlling a team of robotic bodyguards protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders. This task is part of a much larger class of problems involving coordinated robot behavior in the presence of humans. This problem is challenging due to the large number of active...

Many elderly persons and patients recovering from trauma use four legged walkers to regain or retain mobility. Unfortunately, these walkers are also associated with many injuries, some of which are caused due to incorrect use. In this paper, we describe a sensor augmented walker, where the walker continuously monitors gait information. We describe...

Robots assisting the disabled or elderly must perform complex manipulation tasks and must adapt to the home environment and preferences of their user. Learning from demonstration is a promising choice, that would allow the non-technical user to teach the robot different tasks. However, collecting demonstrations in the home environment of a disabled...

We describe a computational model of social norms based on identifying values that a certain culture finds desirable such as dignity, generosity and politeness. The model quantifies these values in the form of Culture-Sanctioned Social Metrics (CSSMs) and treats social norms as the requirement to maximize these metrics from the perspective of the s...

Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering information from the past is criti...

Will the Internet of Things happen? Clearly, the hardware and software components comprising the Internet of Things are technologically feasible, but the sweeping adoption we envision might not take place. The success of technological innovations depends on the creation of a business model that both customers and providers perceive as beneficial. A...

In this paper, we propose a multi-task learning from demonstration method that works using raw images as input to autonomously accomplish a wide variety of tasks in the real world using a low-cost robotic arm. The controller is a single recurrent neural network that can generate robot arm trajectories to perform different manipulation tasks. In ord...

We consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scena...

Side-sweep accidents are one of the major causes of loss of life and property damage on highways. This type of accident is caused by a driver initiating a lane change while another vehicle is blocking the road in the target lane. In this article, we are trying to quantify the degree to which different implementations of vehicle-to-vehicle (V2V) com...

In animal monitoring applications, both animal detection and their movement prediction are major tasks. While a variety of animal monitoring strategies exist, most of them rely on mounting devices. However, in real world, it is difficult to find these animals and install mounting devices. In this paper, we propose an animal monitoring application b...

Traditionally, heavy computational tasks were performed on a dedicated infrastructure requiring a heavy initial investment, such as a supercomputer or a data center. Grid computing relaxed the assumptions of the fixed infrastructure, allowing the sharing of remote computational resources. Cloud computing brought these ideas into the commercial real...

In this paper we focus on a scenario where one or several robotic bodyguards protect a VIP moving in a public environment from physical assaults. To provide maximal physical protection, the robotic bodyguards need to consider the movement of the crowd as well as the obstacles in the environment. We propose two algorithms: Threat Vector Resolution (...

This study investigates the impact of the spatial dimensions and angle of orientation of automobile blind spots during side-sweep accidents. A simulation framework was created that models in detail the situational awareness and decisions of the driver (e.g., checking mirrors before initiating a lane change). The simulation takes into account the de...

Robots assisting disabled or elderly people in the performance of activities of daily living need to perform complex manipulation tasks which are highly dependent on the environment and preferences of the user. In addition, these environments and users are not suitable for the collection of massive amounts of training data, as the manipulated objec...

In this paper, we focus on an application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV). The aim of the application is to detect the locations of endangered species in large-scale wildlife areas or monitor movement of animals without any attachment devices. We first define the mathematical model of the animal monitoring prob...

Current state-of-the-art highway traffic flow simulators rely extensively on models using formulas similar to those describing physical phenomena, such as forces, viscosity, or potential fields. These models have been carefully calibrated to represent the overall flow of traffic and they can also be extended to account for the cognitive limitations...

This research describes a highway traffic simulator focused on the safety of lane changes. The simulator models in detail the situational awareness and behavior of the driver, including the visibility, windows, mirrors, and blind spots of the vehicle as well as the times of checking the mirrors and initiating a lane change. Using this simulator, a...

In this paper we consider a scenario where one or more robotic bodyguards are protecting a VIP moving in a public space against harassment or harm from unarmed civilians. In this scenario, the main objective of the robots is to position themselves such that at any given moment they provide maximum physical cover for the VIP. The robots need to foll...

Sensor networks operating in the field might be subject to catastrophic events which destroy a large number of nodes in the geographic area. Often, the aftermath of such an event is the creation of a network of bridged fragments where connectivity is maintained by one or several bridge nodes. These networks are vulnerable, because the bridge nodes...

Underwater sensor networks (UWSNs) face specific challenges due to the transmission properties in the underwater environment. Radio waves propagate only for short distances under water, and acoustic transmissions have limited data rate and relatively high latency. One of the possible solutions to these challenges involves the use of autonomous unde...

The ability to predict the unfolding of future events is an important feature of any situated AGI system. The most widely used approach is to create a model of the world, initialize it with the desired start state and use it to simulate possible future scenarios. In this paper we propose an alternative approach where there is no explicit model buil...

Geographical routing can provide significant advantages in wireless sensor networks. However in many sensor networks, it is difficult or costly to find the exact location of the nodes. The virtual coordinate techniques allow a network to acquire a coordinate system without relying on geographical location. In this paper, we describe MS-DVCR, an ext...

Current state-of-the-art AI algorithms outperform humans on several well delimited tasks but have difficulty emulating general human behavior. One of the reasons for this is that human behavior, even in short scenarios, requires the integration of multiple cognitive mechanisms that are deployed simultaneously and are interacting with each other in...

This paper considers underwater wireless sensor networks (UWSNs) for submarine surveillance and monitoring. Nodes produce data with an associated value, decaying in time. An autonomous underwater vehicle (AUV) is sent to retrieve information from the nodes, through optical communication, and periodically emerges to deliver the collected data to a s...

Recent developments in mobile robotics made feasible the near future scenario of mobile robots assisting individual persons. Such robots must maintain a sufficient distance from their human owners to be able to offer assistance, but otherwise they need to be inconspicuous and observe the prevailing social and cultural norms. We are considering a sc...

The ability to manipulate social and cultural values in order to achieve one's own goals is a hard-to-teach but profitable skill. In this paper we represent a complex social scenario, the Spanish Steps flower selling scam, using a social calculus framework based on culture sanctioned social metrics (CSSMs) and concrete beliefs (CBs). Then, we show...

We consider an underwater wireless sensor network where baseline communication happens over acoustic, multi-hop routes from the underwater nodes to an on-shore station. The data collected by the nodes greatly exceeds the baseline communication capability. At best, the nodes can transmit digests of their full observations. In order for the sink to r...

We have developed a multi-agent negotiation system to distribute decision making in cognitive radio networks through argumentation. The challenge in wireless network negotiation is to efficiently exchange information to facilitate a deal without incurring excessive communication overhead or indeterminate negotiation time. Our goal is to improve bot...

This paper proposes a reporting decision protocol called IVE (for Information Value -Energy tradeoff), where individual nodes of an intruder tracking sensor network make decisions about the transmission of information chunks. Instead of trying to achieve raw data metrics (such as total transmitted data) the protocol aims to optimize the value of in...

We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real world geographic data, lifestyle-...

Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interf...

The Xapagy cognitive architecture had been designed to perform narrative reasoning: to model and mimic the activities performed by humans when witnessing, reading, recalling, narrating and talking about stories. Xapagy communicates with the outside world using Xapi, a simplified, "pidgin" language which is strongly tied to the internal representati...

Mobile robots moving in a crowd need to conform to the same social standards as the human participants. Imitating human behavior is a natural choice in these situations - however, not every human behaves in the same way. On the other hand, it is known that humans tend to behave in a consistent way, with their behavior predictable by their social st...

In this paper we are considering an autonomous robot moving purposefully in a crowd of people (a marketplace). The robot should take into consideration the social costs of its movement, expressed in terms of violation of the personal space of the humans, blocking their path or even making physical contact with them. On the other hand, the full avoi...

Physical or simulated agents sharing an environment with humans must evaluate the impact of their own and other agents' actions in the specific social and cultural context. It is desirable that this social calculus aligns itself with the models developed in sociology and psychology - however, it needs to be expressed in an operational, algorithmic...

In this paper we develop an operational, quantitative method for the propagation of public perception. The model is presented as an extension of the culture-sanctioned social metric framework. We use the technique to model an extended version of the Spanish Steps flower selling scam, where a seller manipulates the belief of the clients and the publ...

Many cognitive systems deploy multiple, closed, individually consistent models which can represent interpretations of the present state of the world, moments in the past, possible futures or alternate versions of reality. While they appear under different names, these structures can be grouped under the general term of worlds. The Xapagy architectu...

This paper argues that the problem of identity is a critical challenge in agents which are able to reason about stories. The Xapagy architecture has been built from scratch to perform narrative reasoning and relies on a somewhat unusual approach to represent instances and identity. We illustrate the approach by a representation of the story of Litt...

The Xapagy architecture is a story-oriented cognitive system which relies exclusively on the autobiographical memory implemented as a raw collection of events. Reasoning is performed by shadowing current events with events from the autobiography. The shadows are then extrapolated into headless shadows (HLSs). In a story following mood, HLSs can be...

Cognitive radio networks allow for a more dynamic allocation of network resources (such as the radio spectrum) compared to conventionally engineered networks. We consider a scenario where each node acts as an autonomous agent, maintaining a knowledge base of the network conditions and pursuing its own communication goals. The agents use negotiation...

We model in detail a short human interaction scenario, the Spanish Steps flower scam. The scenario involves elements of negotiated commercial transaction, deceit, clash of cultural values and manipulation of public perception. The behavior of the actors is difficult to fit into a model of utility maximizing agents (even if we allow for bounded rati...

Sensor networks are distributed systems where nodes embedded in the environment collect readings through their sensors and transmit data to customers. The overall goal of these systems can be stated as maximizing a metric of the sensing quality while limiting the consumption of a set of scarce resources. In this paper we consider an intruder detect...