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Multi-agent planning and control is an active and increasingly studied topic of research, with many practical applications, such as rescue missions, security, surveillance, and transportation. More specifically, cases that involve complex manipulator-endowed systems  deserve extra attention due to potential complex cooperative manipulation tasks and their interaction with the environment. This thesis addresses the problem of cooperative motion- and task-planning of multi-agent and multi-agent-object systems under complex specifications expressed as temporal logic formulas. We consider manipulator-endowed robotic agents that can coordinate in order to perform, among other tasks, cooperative object manipulation/transportation. Our approach is based on the integration of tools from the following areas: multi-agent systems, cooperative object manipulation, discrete abstraction design of multi-agent-object systems, and formal verification. More specifically, we divide the main problem into three different parts.The first part is devoted to the control design for the formation control of a team of rigid-bodies, motivated by its application to cooperative manipulation schemes. We propose decentralized control protocols such that desired position and orientation-based formation between neighboring agents is achieved. Moreover, inter-agent collisions and connectivity breaks are guaranteed to be avoided. In the second part, we design continuous control laws explicitly for the cooperative manipulation/transportation of an object by a team of robotic agents. Firstly, we propose robust decentralized controllers for the trajectory tracking of the object's center of mass.  Secondly, we design model predictive control-based controllers for the transportation of the object with collision and singularity constraints. In the third part, we design discrete representations of multi-agent continuous systems and synthesize hybrid controllers for the satisfaction of complex tasks expressed as temporal logic formulas. We achieve this by combining the results of the previous parts and by proposing appropriate trajectory tracking- and potential field-based continuous control laws for the transitions of the agents among the discrete states. We consider teams of unmanned aerial vehicles and mobile manipulators as well as multi-agent-object systems where the specifications of the objects are also taken into account.Numerical simulations and experimental results verify the claimed results. / <p>QC 20180219</p>

Dynamical systems that provably satisfy given specifications have become increasingly important in many engineering areas. For instance, safety-critical systems such as human-robot networks or autonomous driving systems are required to be safe and to also satisfy some complex specifications that may include timing constraints, i.e., when or in which order some tasks should be accomplished. Temporal logics have recently proven to be a valuable tool for these control systems by providing a rich specification language. Existing temporal logic-based control approaches discretize the underlying dynamical system in space and/or time, which is commonly referred to as the abstraction process. In other words, the continuous dynamical system is abstracted into a finite system representation, e.g., into a finite state automaton. Such approaches may lead to high computational burdens due to the curse of dimensionality, which makes it hard to use them in practice. Especially with respect to multi-agent systems, these methods do not scale computationally when the number of agents increases. We will address this open research question by deriving abstraction-free control methods for single- and multi-agent systems under signal temporal logic tasks. Another aim of this research is to consider robustness, which is partly taken care of by the robust semantics admitted by signal temporal logic as well as by the robustness properties of the derived control methods. In this work, we propose computationally-efficient frameworks that deal with the aforementioned problems for single- and multi-agent systems by using feedback control strategies such as optimization-based techniques, prescribed performance control, and control barrier functions in combination with hybrid systems theory that allows us to model some higher level decision-making. In each of these approaches, the temporal properties of the employed control methods are used to impose a temporal behavior on the closed-loop system dynamics, which eventually results in the satisfaction of the signal temporal logic task. With respect to the multi-agent case, we consider a bottom-up approach where each agent is subject to a local (individual) task. These tasks may depend on the behavior of other agents. Hence, the multi-agent system is subject to couplings induced on the task level as well as on the dynamical level. The main challenge then is to deal with these couplings and derive control methods that can still satisfy the given tasks or alternatively result in least violating solutions. The efficacy of the theoretical findings is demonstrated in simulations of single- and multi-agent systems under complex specifications. / <p>QC 20180502</p>

A Bayesian network approach to self-organization and learning is introduced for use with intelligent agents. Bayesian networks, with the help of influence diagrams, are employed to create a decision-theoretic intelligent agent. Influence diagrams combine both Bayesian networks and utility theory. In this research, an intelligent agent is modeled by its belief, preference, and capabilities attributes. Each agent is assumed to have its own belief about its environment. The belief aspect of the intelligent agent is accomplished by a Bayesian network. The goal of an intelligent agent is said to be the preference of the agent and is represented with a utility function in the decision theoretic intelligent agent. Capabilities are represented with a set of possible actions of the decision-theoretic intelligent agent. Influence diagrams have utility nodes and decision nodes to handle the preference and capabilities of the decision-theoretic intelligent agent, respectively. Learning is accomplished by Bayesian networks in the decision-theoretic intelligent agent. Bayesian network learning methods are discussed intensively in this paper. Because intelligent agents will explore and learn the environment, the learning algorithm should be implemented online. None of the existent Bayesian network learning algorithms has online learning. Thus, an online Bayesian network learning method is proposed to allow the intelligent agent learn during its exploration. Self-organization of the intelligent agents is accomplished because each agent models other agents by observing their behavior. Agents have belief, not only about environment, but also about other agents. Therefore, an agent takes its decisions according to the model of the environment and the model of the other agents. Even though each agent acts independently, they take the other agents behaviors into account to make a decision. This permits the agents to organize themselves for a common task. To test the proposed intelligent agent's learning and self-organizing abilities, Windows application software is written to simulate multi-agent systems. The software, IntelliAgent, lets the user design decision-theoretic intelligent agents both manually and automatically. The software can also be used for knowledge discovery by employing Bayesian network learning a database. Additionally, we have explored a well-known herding problem to obtain sound results for our intelligent agent design. In the problem, a dog tries to herd a sheep to a certain location, i.e. a pen. The sheep tries to avoid the dog by retreating from the dog. The herding problem is simulated using the IntelliAgent software. Simulations provided good results in terms of the dog's learning ability and its ability to organize its actions according to the sheep's (other agent) behavior. In summary, a decision-theoretic approach is applied to the self-organization and learning problems in intelligent agents. Software was written to simulate the learning and self-organization abilities of the proposed agent design. A user manual for the software and the simulation results are presented. This research is supported by the Office of Naval Research with the grant number N00014-98-1-0779. Their financial support is greatly appreciated. / Ph. D.

Hoy en día los sistemas informáticos complejos se pueden ven en términos de los servicios que ofrecen y las entidades que interactúan para proporcionar o consumir dichos servicios. Los sistemas multi-agente abiertos, donde los agentes pueden entrar o salir del sistema, interactuar y formar grupos (coaliciones de agentes u organizaciones) de forma dinámica para resolver problemas, han sido propuestos como una tecnología adecuada para implementar este nuevo paradigma informático. Sin embargo, el amplio dinamismo de estos sistemas requiere que los agentes tengan una forma de armonizar los conflictos que surgen cuando tienen que colaborar y coordinar sus actividades. En estas situaciones, los agentes necesitan un mecanismo para argumentar de forma eficiente (persuadir a otros agentes para que acepten sus puntos de vista, negociar los términos de un contrato, etc.) y poder llegar a acuerdos. La argumentación es un medio natural y efectivo para abordar los conflictos y contradicciones del conocimiento. Participando en diálogos argumentativos, los agentes pueden llegar a acuerdos con otros agentes. En un sistema multi-agente abierto, los agentes pueden formar sociedades que los vinculan a través de relaciones de dependencia. Estas relaciones pueden surgir de sus interacciones o estar predefinidas por el sistema. Además, los agentes pueden tener un conjunto de valores individuales o sociales, heredados de los grupos a los que pertenecen, que quieren promocionar. Las dependencias entre los agentes y los grupos a los que pertenecen y los valores individuales y sociales definen el contexto social del agente. Este contexto tiene una influencia decisiva en la forma en que un agente puede argumentar y llegar a acuerdos con otros agentes. Por tanto, el contexto social de los agentes debería tener una influencia decisiva en la representación computacional de sus argumentos y en el proceso de gestión de argumentos. / Heras Barberá, SM. (2011). Case-Based Argumentation in Agent Societies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12497

Connected and Automated Vehicles (CAVs) have the potential to revolutionize transportation efficiency and safety through collaborative behavior. This dissertation explores the challenges and opportunities associated with achieving socially optimal cooperative maneuvers, using the problem of cooperative lane-changing to showcase the significance of cooperativeness. Cooperative lane-changing serves as an ideal testbed for examining decentralized optimal control, interactions with uncooperative vehicles, accommodating diverse human driving preferences, and integrating planning and execution processes. Initially, the research focuses on scenarios where all vehicles are cooperative CAVs, leveraging their communication and coordination capabilities. Decentralized optimal control problems are formulated to minimize energy consumption, travel time, and traffic disruption during sequential cooperative lane changes, balancing individual vehicle objectives with system-level goals. The dissertation then extends the analysis to mixed-traffic scenarios involving uncooperative human-driven vehicles (HDVs). A novel approach is developed to ensure safety assurance, combining optimal control with Control Barrier Functions (CBFs) and fixed-time convergence (FxT-OCBF). Robust methods for handling disturbances from uncooperative vehicles are introduced, enhancing the resilience and dependability of cooperative lane-changing maneuvers. An innovative online learning framework is presented to address the complexities of CAVs interacting with HDVs exhibiting diverse driving preferences. Safety preferences are characterized using parameterized CBFs, and an extended Kalman filter dynamically adjusts control parameters based on observed interactions, enabling real-time adaptation to evolving human behaviors. The proposed methodologies bridge the gap between high-level planning and low-level control execution, facilitating safe and near-optimal cooperative maneuvers. Comprehensive analysis demonstrates improved traffic throughput, reduced energy consumption, and enhanced safety compared to non-cooperative or reactive approaches. This research lays the foundation for deploying CAV technologies that prioritize social optimality while addressing uncertainties in mixed-traffic settings, ultimately paving the way for safer and more efficient transportation systems. / 2025-03-11T00:00:00Z

Recent events have probably lead us to wonder why people make decisions that seem to be irrational, and that go against any easily understandable logic. The fact that these decisions are emotionally driven often explains what, at first glance, does not have a plausible explanation. Evidence has been found that proves that emotions and other affective characteristics guide decisions beyond a purely rational deliberation. Understanding the way emotions take place, the way emotions change, and/or the way emotions influence behavior, has traditionally been a concern of several fields including psychology and neurology. Moreover, other sciences such as behavioral economics, artificial intelligence, and in general, all sciences that aim to understand, explain, or simulate human behavior, acknowledge the important role of affective characteristics in this task. Specifically, artificial intelligence uses psychological findings in order to create agents that simulate human behavior. Nevertheless, individual research efforts in modeling affective characteristics are often overlapped, short of integration, and they lack of a common conceptual system. This deprives individual researches of the exchange and cooperation's inherent benefits, and makes the task of computationally simulating affective characteristics more difficult. Although much individual effort has been put in classifying, formalizing and modeling emotions and emotion theories on some fields, recognized researchers of emotions' and affective processes' modeling report that a common formal language, an informal conceptual system, and a general purpose affective agent architecture will greatly improve the interdisciplinary exchange and the intradisciplinary coordination. The research literature proposes a wide amount of affective models that deal with some of: relationship between emotions and cognition, relationship between emotions and behavior, emotions and their evolutionary account, emotions for appraising situations, emotion regulation, etc. These models are useful tools for addressing particular emotion-related issues. Furthermore, computational approaches that are based on particular psychological theories have also been proposed. They often address domain specific issues starting from a specific psychological theory. In such solutions, the absence of a common conceptual system and/or platform, makes difficult the feedback between psychological theories and computational approaches. This thesis systematizes and formalizes affect-related theories, what can benefit the interdisciplinary exchange, the intradisciplinary coordination, and hence, allows the improvement of involved disciplines. Specifically this thesis makes the following contributions: (1) a theoretical framework that includes the main processes and concepts that a model of an affective agent with practical reasoning should have; (2) a general-purpose affective agent architecture that shares the concepts of the proposed theoretical framework; (3) an implementation-independent formal language for designing affective agents that have the proposed architecture; and (4) a specific agent language for implementing affective agents which is an extension of a BDI language. Some studies with human participants have helped to validate the contributions of this thesis. They include classical games of game theory, and an study with 300 participants, which have provided the necessary information to evaluate the contributions. The validation has been performed in three directions: determine whether the proposed computational approach represents better the human behavior than traditional computational approaches; determine whether this approach allows to improve psychological theories used by default; and determine whether the proposed affective agents' behavior is closer to human behavior than the behavior of a purely rational agent. / Probablemente algunos eventos recientes nos han conducido a preguntarnos por qué las personas toman decisiones aparentemente irracionales y en contra de alguna lógica fácilmente comprensible. El hecho de que estas decisiones estén bajo la influencia de las emociones a menudo explica lo que, a primera vista, parece no tener una explicación aceptable. En este sentido, se han encontrado evidencias que prueban que las emociones y otras características afectivas condicionan las decisiones más allá de una deliberación meramente racional. Entender cómo las emociones tienen lugar, cómo cambian y cómo influyen en el comportamiento, ha sido tradicionalmente de interés para muchos campos de investigación, incluyendo la psicología y la neurología. Además, otras ciencias como la economía conductual o la inteligencia artificial reconocen el importante papel de las características afectivas en esta tarea. Específicamente, la inteligencia artificial utiliza los resultados obtenidos en psicología para crear agentes que simulan el comportamiento humano. Sin embargo, a menudo los esfuerzos individuales de investigación en el modelado del afecto se solapan, carecen de la suficiente integración y de un sistema conceptual común. Esto limita a las investigaciones individuales para disponer de los beneficios que ofrecen el intercambio y la cooperación, y hace más compleja la tarea de simular los procesos afectivos. Las emociones y teorías relacionadas han sido clasificadas, formalizadas y modeladas. No obstante, reconocidos investigadores argumentan que un lenguaje formal común, un sistema conceptual informal y una arquitectura de agentes de propósito general, mejorarán significativamente el intercambio interdisciplinar y la coordinación intradisciplinar. En la literatura se propone una amplia cantidad de modelos afectivos que modelan: la relación entre las emociones y la cognición, la relación entre las emociones y el comportamiento, las emociones para evaluar las situaciones, la regulación de emociones, etc. Estos modelos son herramientas útiles para abordar aspectos particulares relacionados con las emociones. Además, se han realizado propuestas computacionales que abordan aspectos específicos sobre la base de teorías psicológicas específicas. En éstas soluciones, la ausencia de una plataforma y/o sistema conceptual dificulta la retroalimentación entre las teorías psicológicas y las propuestas computacionales. Esta tesis sistematiza y formaliza teorías relacionadas con el afecto, lo cual beneficia el intercambio interdisciplinar y la coordinación intradisciplinar, y por tanto, permite el desarrollo de las disciplinas correspondientes. Específicamente esta tesis realiza las siguientes contribuciones: (1) una plataforma teórica que incluye los conceptos y procesos principales que debería poseer un modelo de agentes afectivos con razonamiento práctico; (2) una arquitectura de agentes de propósito general que comparte los conceptos de la plataforma teórica propuesta; (3) un lenguaje formal independiente de la implementación, para diseñar agentes afectivos que poseen la arquitectura propuesta; y (4) un lenguaje de agentes específico para implementar agentes afectivos el cual es un extensión de un lenguaje BDI. Algunos estudios con participantes humanos han ayudado a validar las contribuciones de esta tesis. Estos incluyen juegos clásicos de teoría de juegos y un estudio con 300 participantes, los cuales han proporcionado la información necesaria para evaluar las contribuciones. La validación se ha realizado en tres direcciones: determinar si la propuesta computacional que se ha realizado representa mejor el comportamiento humano que propuestas computacionales tradicionales; determinar si esta propuesta permite mejorar las teorías psicológicas empleadas por defecto; y determinar si el comportamiento de los agentes afectivos propuestos se acerca más al comportamiento humano que el compor / Probablement alguns esdeveniments recents ens han conduït a preguntar-nos per què les persones prenen decisions que aparentment són irracionals i que van en contra d'algun tipus de lògica fàcilment comprensible. El fet que aquestes decisions estiguin sota la influència de les emocions sovint explica el que, a primera vista, sembla no tenir una explicació acceptable. En aquest sentit, s'han trobat evidències que proven que les emocions i altres característiques afectives condicionen les decisions més enllà d'una deliberació merament racional. Entendre com les emocions tenen lloc, com canvien i com influeixen en el comportament, ha estat tradicionalment d'interès per a molts camps d'investigació, incloent la psicologia i la neurologia. A més, altres ciències com l'economia conductual, la intel·ligència artificial i, en general, totes les ciències que intenten entendre, explicar o simular el comportament humà, reconeixen l'important paper de les característiques afectives en aquesta tasca. Específicament, la intel·ligència artificial utilitza els resultats obtinguts en psicologia per crear agents que simulen el comportament humà. No obstant això, sovint els esforços individuals d'investigació en el modelatge de l'afecte es solapen, no tenen la suficient integració ni compten amb un sistema conceptual comú. Això limita a les investigacions individuals, que no poden disposar dels beneficis que ofereixen l'intercanvi i la cooperació, i fa més complexa la tasca de simular els processos afectius. Les emocions i teories relacionades han estat classificades, formalitzades i modelades. No obstant això reconeguts investigadors argumenten que un llenguatge formal comú, un sistema conceptual informal i una arquitectura d'agents de propòsit general, milloraran significativament l'intercanvi interdisciplinar i la coordinació intradisciplinar. En la literatura es proposa una àmplia quantitat de models afectius que modelen: la relació entre les emocions i la cognició, la relació entre les emocions i el comportament, les emocions per avaluar les situacions, la regulació d'emocions, etc. Aquests models són eines útils per abordar aspectes particulars relacionats amb les emocions. A més, s'han realitzat propostes computacionals que aborden aspectes específics sobre la base de teories psicològiques específiques. En aquestes solucions, l'absència d'una plataforma i/o sistema conceptual dificulta la retroalimentació entre les teories psicològiques i les propostes computacionals. Aquesta tesi sistematitza i formalitza teories relacionades amb l'afecte, la qual cosa beneficia l'intercanvi interdisciplinar i la coordinació intradisciplinar, i per tant, permet el desenvolupament de les disciplines corresponents. Específicament aquesta tesi realitza les següents contribucions: (1) una plataforma teòrica que inclou els conceptes i processos principals que hauria de posseir un model d'agents afectius amb raonament pràctic; (2) una arquitectura d'agents de propòsit general que comparteix els conceptes de la plataforma teòrica proposta; (3) un llenguatge formal independent de la implementació, per dissenyar agents afectius que posseeixen l'arquitectura proposada; i (4) un llenguatge d'agents específic per implementar agents afectius el qual és un extensió d'un llenguatge BDI. Alguns estudis amb participants humans han ajudat a validar les contribucions d'aquesta tesi. Aquests inclouen jocs clàssics de teoria de jocs i un estudi amb 300 participants, els quals han proporcionat la informació necessària per avaluar les contribucions. La validació s'ha realitzat en tres direccions: determinar si la proposta computacional que s'ha realitzat representa millor el comportament humà que propostes computacionals tradicionals; determinar si aquesta proposta permet millorar les teories psicològiques emprades per defecte; i determinar si el comportament dels agents afectius proposats s'acosta més al / Alfonso Espinosa, B. (2017). Agents with Affective Traits for Decision-Making in Complex Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90497

In nature, many social species demonstrate collective behavior ranging from coordinated motion in flocks of birds and schools of fish to collective decision making in humans. Such distinct behavioral patterns at the group level are the consequence of local interactions among the individuals. We can learn from these biological systems, which have successfully evolved to operate in noisy and fault-prone environments, and understand how these complex interactions can be applied to engineered systems where robustness remains a major challenge. This dissertation addresses a two-scale approach to study these interactions- one in larger scale, where we are interested in the information exchange in a group and how it enables the group to reach a common decision, and the other in a smaller scale, where we are focused in the presence and directionality in the information exchange in a pair of individuals. To understand the interactions at large scale, we use a graph theoretic approach to study consensus or synchronization protocols over two types of biologically-inspired interaction networks. The first network captures both collaborative and antagonistic interactions and the second considers the impact of dynamic leaders in presence of purely collaborative interactions. To study the interactions at small scale, we use an information theoretic approach to understand the directionality of information transfer in a pair of individual using a real-world data-set of animal group motion. Finally, we choose the issue of same-sex marriage in the United States to demonstrate that collective opinion formation is not only a result of negotiations among the individuals, but also reflects inherent spatial and political similarities and temporal delays. / Ph. D. / Social animals exhibit coordination often referred to as ‘collective behavior’ that results from interactions among individuals in the group. This dissertation has demonstrated how interactions can be studied using mathematical modeling, at the same time reveals that real-world interactions are even more complex. Mathematical modeling provides capabilities to introduce biologically inspired phenomena, for example, the implementation of both friendly and hostile interactions that may coexist; and the presence of leader-follower interactions, which is another determinant of collective behavior. The results may find applications in real-world networks, where hostile and leader-follower interactions are prevalent, for example international relations, online social media sites, neural networks, and biologically inspired robotic interactions. We further extend our knowledge regarding interactions by choosing real world systems, the first to understand human decision making, for example in public policies; and the second in animal group motion. Public policy adoption is generally complex and depends on a variety of factors, and no exception is same-sex marriage in the United States which has been a volatile subject for decades until nationwide legalization on June 26, 2015. We target this timely issue and explore the opinion formation of senators and state-law as they evolve over two decades to identify factors that may have affected the dynamics. We unravel geographic proximity, and state-government ideology are significant contributors to the senators opinions and the state-law adoption. Moreover, we build a state-law adoption model which captures these driving factors, and demonstrates predictive power. This study will help to understand or model other public policies that propagate via social and political change. Next we choose the system of bats to investigate navigational leadership roles as they fly in pairs from direct observation of bat swarms in flight. Pairs of bats were continuously tracked in a mountain cave in Shandong Province, China, from which three-dimensional path points are extracted and converted to one-dimensional curvature time series. The study allows us to answer the question of whether individuals fly independently of each other or interact to plan flight paths.

Past research on multi-agent simulation with cooperative reinforcement learning (RL) for homogeneous agents focuses on developing sharing strategies that are adopted and used by all agents in the environment. These sharing strategies are considered to be reciprocating because all participating agents have a predefined agreement regarding what type of information is shared, when it is shared, and how the participating agent's policies are subsequently updated. The sharing strategies are specifically designed around manipulating this shared information to improve learning performance. This thesis targets situations where the assumption of a single sharing strategy that is employed by all agents is not valid. This work seeks to address how agents with no predetermined sharing partners can exploit groups of cooperatively learning agents to improve learning performance when compared to Independent learning. Specifically, several intra-agent methods are proposed that do not assume a reciprocating sharing relationship and leverage the pre-existing agent interface associated with Q-Learning to expedite learning. The other agents' functions and their sharing strategies are unknown and inaccessible from the point of view of the agent(s) using the proposed methods. The proposed methods are evaluated on physically embodied agents in the multi-agent cooperative robotics field learning a navigation task via simulation. The experiments conducted focus on the effects of the following factors on the performance of the proposed non-reciprocating methods: scaling the number of agents in the environment, limiting the communication range of the agents, and scaling the size of the environment. / Master of Science