The Dynamics of Intentions in Collaborative Intentionality

An adequate formulation of collective intentionality is crucial for understanding group activity and for modeling the mental state of participants in such activities. Although work on collective intentionality in philosophy, artificial intelligence, and cognitive science has many points of agreement, several key issues remain under debate. This paper argues that the dynamics of intention – in particular, the inter-related processes of plan-related group decision making and intention updating – play crucial roles in an explanation of collective intentionality. Furthermore, it is in these dynamic aspects that coordinated group activity differs most from individual activity. The paper specifies a model of the dynamics of agent intentions in the context of collaborative activity. Its integrated treatment of group decision making and coordinated updating of group-related intentions fills an important gap in prior accounts of collective intentionality, thus helping to resolve a long-standing debate about the nature of intentions in group activity. The paper also defines an architecture for collaboration-capable computer agents that satisfies the constraints of the model and is a natural extension of the standard architecture for resource-bounded agents operating as individuals. The new architecture is both more principled and more complete than prior architectures for collaborative multi-agent systems.Engineering and Applied Science

Automated highway systems : platoons of vehicles viewed as a multiagent system

Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2005-2006La conduite collaborative est un domaine lié aux systèmes de transport intelligents, qui utilise les communications pour guider de façon autonome des véhicules coopératifs sur une autoroute automatisée. Depuis les dernières années, différentes architectures de véhicules automatisés ont été proposées, mais la plupart d’entre elles n’ont pas, ou presque pas, attaqué le problème de communication inter véhicules. À l’intérieur de ce mémoire, nous nous attaquons au problème de la conduite collaborative en utilisant un peloton de voitures conduites par des agents logiciels plus ou moins autonomes, interagissant dans un même environnement multi-agents: une autoroute automatisée. Pour ce faire, nous proposons une architecture hiérarchique d’agents conducteurs de voitures, se basant sur trois couches (couche de guidance, couche de management et couche de contrôle du trafic). Cette architecture peut être utilisée pour développer un peloton centralisé, où un agent conducteur de tête coordonne les autres avec des règles strictes, et un peloton décentralisé, où le peloton est vu comme une équipe d’agents conducteurs ayant le même niveau d’autonomie et essayant de maintenir le peloton stable.Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) that makes use of communications to autonomously guide cooperative vehicles on an Automated Highway System (AHS). For the past decade, different architectures of automated vehicles have been proposed, but most of them did not or barely addressed the inter-vehicle communication problem. In this thesis, we address the collaborative driving problem by using a platoon of cars driven by more or less autonomous software agents interacting in a Multiagent System (MAS) environment: the automated highway. To achieve this, we propose a hierarchical driving agent architecture based on three layers (guidance layer, management layer and traffic control layer). This architecture can be used to develop centralized platoons, where the driving agent of the head vehicle coordinates other driving agents by applying strict rules, and decentralized platoons, where the platoon is considered as a team of driving agents with a similar degree of autonomy, trying to maintain a stable platoon

Agents and Robots for Reliable Engineered Autonomy

This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

Agent-oriented constructivist knowledge management

In Ancient Times, when written language was introduced, books and manuscripts were often considered sacred. During these times, only a few persons were able to read and interpret them, while most people were limited in accepting these interpretations. Then, along with the industrial revolution of the XVIII and XIX centuries and especially boosted by the development of the press, knowledge slowly became available to all people. Simultaneously, people were starting to apply machines in the development of their work, usually characterized by repetitive processes, and especially focused in the production of consuming goods, such as furniture, clocks, clothes and so on. Following the needs of this new society, it was finally through science that new processes emerged to enable the transmission of knowledge from books and instructors to learners. Still today, people gain knowledge based on these processes, created to fulfill the needs of a society in its early stages of industrialization, thus not being compatible with the needs of the information society. In the information society, people must deal with an overloading amount of information, by the means of the media, books, besides different telecommunication and information systems technology. Furthermore, people’s relation to work has been influenced by profound changes, for instance, knowledge itself is now regarded as a valuable work product and, thus, the workplace has become an environment of knowledge creation and learning. Modifications in the world economical, political and social scenarios led to the conclusion that knowledge is the differential that can lead to innovation and, consequently, save organizations, societies, and even countries from failing in achieving their main goals. Focusing on these matters is the Knowledge Management (KM) research area, which deals with the creation, integration and use of knowledge, aiming at improving the performance of individuals and organizations. Advances in this field are mainly motivated by the assumption that organizations should focus on knowledge assets (generally maintained by the members of an organization) to remain competitive in the information society’s market. This thesis argues that KM initiatives should be targeted based on a constructivist perspective. In general, a constructivist view on KM focuses on how knowledge emerges, giving great importance to the knowledge holders and their natural practices. With the paragraph above, the reader may already have an intuition of how this work faces and targets Knowledge Management, however, let us be more precise. Research in Knowledge Management has evolved substantially in the past 30 years, coming from a centralized view of KM processes to a distributed view, grounded in organizational and cognitive sciences studies that point out the social, distributed, and subjective nature of knowledge. The first Knowledge Management Systems (KMSs) were centrally based and followed a top-down design approach. The organization managers, supported by knowledge engineers, collected and structured the contents of an organizational memory as a finished product at design time (before the organizational memory was deployed) and then disseminated the product, expecting employees to use it and update it. However, employees often claimed that the knowledge stored in the repository was detached from their real working practices. This led to the development of evolutionary methods, which prescribe that the basic KM system is initially developed and evolves proactively in an on-going fashion. However, most of the initiatives are still based on building central repositories and portals, which assume standardized vocabularies, languages, and classification schemes. Consequently, employees’ lack of trust and motivation often lead to dissatisfaction. In other words, workers resist on sharing knowledge, since they do not know who is going to access it and what is going to be done with it. Moreover, the importance attributed to knowledge may give an impression that these central systems take away a valuable asset from his or her owner, without giving appreciable benefits in return. The problems highlighted in the previous paragraph may be attenuated or even solved if a top-down/bottom-up strategy is applied when proposing a KM solution. This means that the solution should be sought with aim at organizational goals (top-down) but at the same time, more attention should be given to the knowledge holders and on the natural processes they already use to share knowledge (bottom-up). Being active agency such an important principle of Constructivism, this work recognizes that the Agent Paradigm (first defined by Artificial Intelligence and more recently adopted by Software Engineering) is the best approach to target Knowledge Management, taking a technological and social perspective. Capable of modeling and supporting social environments, agents is here recognized as a suitable solution for Knowledge Management especially by providing a suitable metaphor used for modeling KM domains (i.e. representing humans and organizations) and systems. Applying agents as metaphors on KM is mainly motivated by the definition of agents as cognitive beings having characteristics that resemble human cognition, such as autonomy, reactivity, goals, beliefs, desires, and social-ability. Using agents as human abstractions is motivated by the fact that, for specific problems, such as software engineering and knowledge management process modeling, agents may aid the analyst to abstract away from some of the problems related to human complexity, and focus on the important issues that impact the specific goals, beliefs and tasks of agents of the domain. This often leads to a clear understanding of the current situation, which is essential for the proposal of an appropriate solution. The current situation may be understood by modeling at the same time the overall goals of the organization, and the needs and wants of knowledge holders. Towards facilitating the analysis of KM scenarios and the development of adequate solutions, this work proposes ARKnowD (Agent-oriented Recipe for Knowledge Management Systems Development). Systems here have a broad definition, comprehending both technology-based systems (e.g. information system, groupware, repositories) and/or human systems, i.e. human processes supporting KM using non-computational artifacts (e.g. brain stormings, creativity workshops). The basic philosophical assumptions behind ARKnowD are: a) the interactions between human and system should be understood according to the constructivist principle of self-construction, claiming that humans and communities are self-organizing entities that constantly construct their identities and evolve throughout endless interaction cycles. As a result of such interactions, humans shape systems and, at the same time, systems constrain the ways humans act and change; b) KM enabling systems should be built in a bottom-up approach, aiming at the organizational goals, but understanding that in order to fulfill these goals, some personal needs and wants of the knowledge holders (i.e. the organizational members) need to be targeted; and c) there is no “silver bullet��? when pursuing a KM tailoring methodology and the best approach is combining existing agent-oriented approaches according to the given domain or situation. This work shows how the principles above may be achieved by the integration of two existing work on agent-oriented software engineering, which are combined to guide KM analysts and system developers when conceiving KM solutions. Innovation in our work is achieved by supporting topdown/bottom-up approaches to KM as mentioned above. The proposed methodology does that by strongly emphasizing the earlier phases of software development, the so-called requirement analysis activity. In this way, we consider all stakeholders (organizations and humans) as agents in our analysis model, and start by understanding their relations before actually thinking of developing a system. Perhaps the problem may be more effectively solved by proposing changes in the business processes, rather than by making use of new technology. And besides, in addition to humans and organizations, existing systems are also included in the model from start, helping the analyst and designer to understand which functionalities are delegated to these so-called artificial agents. In addition to that, benefits as a result of the application of ARKnowD may be also attributed to our choice of using the proper agent cognitive characteristics in the different phases of the development cycle. With the main purpose of exemplifying the use of the proposed methodology, this work presents a socially-aware recommender agent named KARe (Knowledgeable Agent for Recommendations). Recommender Systems may be defined by those that support users in selecting items of their need from a big set of items, helping users to overcome the overwhelming feeling when facing a vast information source, such as the web, an organizational repository or the like. Besides serving as a case for our methodology, this work also aims at exploring the suitability of the KARe system to support KM processes. Our choice for supporting knowledge sharing through questioning and answering processes is again supported by Constructivism proponents, who understand that social interaction is vital for active knowledge building. This assumption is also defended by some KM theories, claiming that knowledge is created through cycles of transformation between two types of knowledge: tacit and explicit knowledge. Up to now, research on KM has paid much attention to the formalization and exchange of explicit knowledge, in the form of documents or other physical artifacts, often annotated with metadata, and classified by taxonomies or ontologies. Investigations surrounding tacit knowledge have been so far scarce, perhaps by the complexity of the tasks of capturing and integrating such kind of knowledge, defined as knowledge about personal experience and values, usually confined on people’s mind. Taking a flexible approach on supporting this kind of knowledge conversion, KARe relies on the potential of social interaction underlying organizational practices to support knowledge creation and sharing. The global objective of this work is to support knowledge creation and sharing within an organization, according to its own natural processes and social behaviors. In other words, this work is based on the assumption that KM is better supported if knowledge is looked at from a constructivist perspective. To sum up, this thesis aims at: 1) Providing an agent-oriented approach to guide the creation and evolvement of KM initiatives, by analyzing the organizational potentials, behaviors and processes concerning knowledge sharing; 2) Developing the KARe recommender system, based on a semantically enriched Information Retrieval technique for recommending knowledge artifacts, supporting users to ask and answer to each others’ questions. These objectives are achieved as follows: - Defining the principles that characterize a Constructivist KM supporting environment and understanding how they may be used to support the creation of more effective KM solutions; - Providing an agent-oriented approach to develop KM systems. This approach is based on the integration of two different agent-oriented software engineering works, profiting from their strengths in providing a comprehensive methodology that targets both analysis and design activities; - Proposing and designing a socially aware agent-oriented recommender system both to exemplify the application of the proposed approach and to explore its potential on supporting knowledge creation and sharing. - Implementing an Information Retrieval algorithm to support the previously mentioned system in generating recommendations. Besides describing the algorithm, this thesis brings experimental results to prove its effectiveness

Modeling Supervisory Control in Multi Robot Applications

We consider multi robot applications, where a human operator monitors and supervise the team to pursue complex objectives in complex environments. Robots, specially at field sites, are often subject to unexpected events that can not be managed without the intervention of the operator(s). For example, in an environmental monitoring application, robots might face extreme environmental events (e.g. water currents) or moving obstacles (e.g. animal approaching the robots). In such scenarios, the operator often needs to interrupt the activities of individual team members to deal with particular situations. This work focuses on human-multi-robot-interaction in these casts. A widely used approach to monitor and supervise robotic teams are team plans, which allow an operator to interact via high level objectives and use automation to work out the details. The first problem we address in this context, is how human interrupts (i.e. change of action due to unexpected events) can be handled within a robotic team. Typically, after such interrupts, the operator would need to restart the team plan to ensure its success. This causes delays and imposes extra load on the operator. We address this problem by presenting an approach to encoding how interrupts can be smoothly handled within a team plan. Building on a team plan formalism that uses Colored Petri Nets, we describe a mechanism that allows a range of interrupts to be handled smoothly, allowing the team to effectively continue with its task after the operator intervention. We validate the approach with an application of robotic water monitoring. Our experiments show that the use of our interrupt mechanism decreases the time to complete the plan (up to 48% reduction) and decreases the operator load (up to 80% reduction in number of user actions). Moreover, we performed experiments with real robotic platforms to validate the applicability of our mechanism in the actual deployment of robotic watercraft. The second problem we address is how to handle intervention requests from robots to the operator. In this case, we consider autonomous robotic platforms that are able to identify their situation and ask for the intervention of the operator by sending a request. However, large teams can easily overwhelm the operator with several requests, hence hindering the team performance. As a consequence, team members will have to wait for the operator attention, and the operator becomes a bottleneck for the system. Our contribution in this context is to make the robots learn cooperative strategies to best utilize the operator's time and decrease the idle time of the robotic system. In particular, we consider a queuing model (a.k.a balking queue), where robots decide whether or not to join the queue. Such decisions are computed by considering dynamic features of the system (e.g. the severity of the request, number of requests, etc.). We examine several decision making solutions for computing these cooperative strategies, where our goal is to find a trade-off between lower idle time by joining the queue and fewer failures due to the risk of not joining the queue. We validate the proposed approaches in a simulation robotic water monitoring application. The obtained results show the effectiveness of our proposed models in comparison to the queue without balking, when considering team reward and total idle time

Uma nova abordagem para a implementação de um sistema multiagente para a configuração e o monitoramento da produção de pequenas séries

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2014A Produção de Pequenas Séries (PPS) é um tipo de manufatura caracterizado pela alta diversidade de produtos a serem produzidos associada a tamanhos de lotes reduzidos - possivelmente unitários. Neste sistema de produção as tecnologias empregadas para configuração e monitoramento do processo devem priorizar a produção sem defeitos, inclusive no primeiro item do lote, que pode ser o único. Falhas neste sistema de produção ou defeitos inseridos nos produtos facilmente inviabilizam economicamente todo o lote. Neste contexto, a linha de produção deve ser capaz de efetuar uma troca rápida de configuração para produzir um novo lote em um cenário de vários lotes de tamanho reduzido. É preciso também garantir completo monitoramento da produção do lote, sem falhas, ou quando ocorrer uma falha, ações corretivas devem ser executadas imediatamente. As pesquisas recentes demonstram que sistemas baseados em agentes é uma abordagem promissora para o cenário da PPS. Diante disso, a contribuição desta tese é a apresentação de uma nova Arquitetura de Referência para a implementação de Sistemas Multiagente na configuração e monitoramento da Produção de Pequenas Séries. A abordagem propõe uma Arquitetura de Referência, chamada MAS4SSP, baseada em uma solução unificada, sinérgica e com alto nível de abstração. Para garantir isto, a abordagem emprega como Modelo de Referência o framework JaCaMo que segue o paradigma orientado a multiagente (MAOP). A integração com a linha de produção é realizada com o emprego da tecnologia de comunicação Web Service que é utilizada pelo Sistema Multiagente (cliente) e por um sistema SCADA baseado em aplicação Web, o ScadaBR (servidor). A interface com o usuário pode ser desenvolvida como um recurso adicional na plataforma JaCaMo, ou pode ser realizada uma integração com sistemas legados de produção (como ERP, PCP, MRP) utilizando também a tecnologia de Web Service. Além da Arquitetura de Referência a tese apresenta um Modelo Genérico de Modelagem e Implementação que serve como guia para o desenvolvedor. Esta abordagem foi instanciada em um experimento simulado no contexto de uma PPS de Placas de Circuito Impresso (PCI). Por fim, os resultados e as conclusões sobre a Arquitetura de Referência e o Modelo Genérico de Modelagem e Implementação são apresentados em conjunto com sugestões de trabalhos futuros.Abstract: Small Series Production (SSP) is a type of manufacturing characterized by a high diversity of products to be produced associated with a reduced batch sizes - possibly unitary. In this production system, the technologies employed for process setup and monitoring must prioritize production without defects, including the first item of the lot, which may be the one. Faults in this production system or defects in the product easily become the whole batch economically unfeasible. In this context, the production line should be able to monitor the process and make a quick change of the configuration to produce a new batch in a scenario of several batches with small sizes. The system should also ensure the batch production without failure or when a fault occurs, the corrective actions must be executed immediately. Recent research shows that agent-based systems are a promising approach for the SSP. The contribution of this thesis is the presentation of a new Reference Architecture for the implementation of Multi-Agent Systems to setup and monitoring Small Series Production. The approach proposes a Reference Architecture, called MAS4SSP, based on a unified, synergistic and high level of abstraction solution. To ensure this, the approach employs as a Reference Model the JaCaMo framework that follows the Multi-Agent Oriented Paradigm (MAOP). The integration with the production line is realized with the use of Web Service as communication technology between the Multi-Agent System (client) and a SCADA system - ScadaBR (server). The user interface can be developed as an additional resource in JaCaMo platform or can be an integration system with legacy production systems (such as ERP, MES, MRP) also using Web Service. Beyond the Reference Architecture, this thesis presents a Generic Modeling and Implementation Model which serves as a guide for the developer and that was instantiated in a controlled experiment - a SSP line of Printed Circuit Boards (PCB). Finally, the conclusions and perspectives about the Reference Architecture and the Generic Modeling and Implementation Model are presented together with suggestions for future works

Collaboration with agents in VR environments

Virtual reality is gaining on importance in many fields – scientific simulation, training, therapy and also more and more in entertainment. All these applications require the human user to interact with virtual worlds inhabited by intelligent characters and to solve simulated or real problems. This thesis will present an integrated approach to simulated problem solving in virtual reality environments, with the emphasis on teamwork and the ability to control the simulations. A simulation framework satisfying these goals will be presented. A unified approach to the representation of semantic information in virtual environments based on predicate calculus will be introduced, including the representation of the world state, action semantics and basic axioms holding in the simulated world. Afterwards, the focus will be on the collaboration model based on task delegation and facilitator-centric architecture. A simple but efficient facilitator design will be presented. The issues of the collaborative problem solving will be examined. A new technique using propositional (STRIPS-like) planning with delegated actions and object-specific planning will be described. A control technique for virtual characters/objects will be detailed, enabling run-time exchange of control and control sharing over a virtual entity between multiple autonomous agents and/or human users. Finally, a set of case studies will be shown, illustrating the possible applications of the techniques developed and described in this dissertation

Diseño e implementación de una arquitectura multipropósito basada en agentes inteligentes: aplicación a la planificación automática de agendas y al control de procesos

El concepto de agente ha cobrado una enorme importancia en nuestros días siendo aplicado a campos tan diversos como el transporte de material militar, la gestión de información o la reserva de billetes de avión a través de Internet. Sin embargo, a fecha de hoy, se carece de una definición formal aceptada universalmente sobre el mismo. Las definiciones existentes suelen hacer hincapié en características como las de autonomía, toma de la iniciativa e inteligencia. En la mayoría de los casos un único agente no es suficiente para llevar a cabo una tarea compleja. Lo más habitual es emplear un sistema de agentes que se comuniquen entre sí y que colaboren para llevar a cabo esta tarea. Estas sociedades de agentes son los sistemas multiagentes (MAS).Los objetivos de esta tesis son el estudio en profundidad de los aspectos relacionados con el campo de los agentes y su tecnología asociada, la determinación a partir de dicho estudio las tecnologías consideradas como más avanzadas, el desarrollo de una plataforma de agentes que cubra los aspectos iniciales de estas arquitecturas y, finalmente, la aplicación de esta plataforma a dos campos lo suficientemente diversos para demostrar la versatilidad de la arquitectura propuesta: la planificación automática de agendas (organización de reuniones internas y reserva de material) en el escenario de un grupo de investigación universitario y por otra parte la identificación y control de sistemas en tiempo real.Respecto al objetivo del diseño de una plataforma de agentes y tras la realización de un primer prototipo de MAS basado en el intercambio de mensajes KQML, se llegó a la constatación de la existencia de un estandar de arquitectura de MAS que, si bien no cumplía todos los requisitos iniciales del trabajo, sí lo hacía en un alto grado. Dicho estandar es el FIPA, a partir del cual se diseñaron e implementaron los sistemas multiagentes presentados en este trabajo