INVESTIGATING AGENT AND TASK OPENNESS IN ADHOC TEAM FORMATION

When deciding which ad hoc team to join, agents are often required to consider rewards from accomplishing tasks as well as potential benefits from learning when working with others, when solving tasks. We argue that, in order to decide when to learn or when to solve task, agents have to consider the existing agents’ capabilities and tasks available in the environment, and thus agents have to consider agent and task openness—the rate of new, previously unknown agents (and tasks) that are introduced into the environment. We further assume that agents evolve their capabilities intrinsically through learning by observation or learning by doing when working in a team. Thus, an agent will need to consider which task to do or which team to join would provide the best situation for such learning to occur. In this thesis, we develop an auction-based multiagent simulation framework, a mechanism to simulate openness in our environment, and conduct comprehensive experiments to investigate the impact of agent and task openness. We propose several agent task selection strategies to leverage the environmental openness. Furthermore, we present a multiagent solution for agent-based collaborative human task assignment when finding suitable tasks for users in complex environments is made especially challenging by agent openness and task openness. Using an auction-based protocol to fairly assign tasks, software agents model uncertainty in the outcomes of bids caused by openness, then acquire tasks for people that maximize both the user’s utility gain and learning opportunities for human users (who improve their abilities to accomplish future tasks through learning by experience and by observing more capable humans). Experimental results demonstrate the effects of agent and task openness on collaborative task assignment, the benefits of reasoning about openness, and the value of non-myopically choosing tasks to help people improve their abilities for uncertain future tasks

Collaborative environment to support a professional community

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresRecent manufacturing roadmaps stress current production systems limitations, emphasizing social, economic and ecologic consequences for Europe of a non-evolution to sustainable Production Systems. Hence, both academic institutions and enterprises are committed to develop solutions that would endow enterprises to survive in nowadays’ extremely competitive business environment. A research effort is being carried on by the Evolvable Production Systems consortium towards attaining Production Systems that can cope with current technological, economical, ecological and social demands fulfilling recent roadmaps. Nevertheless research success depends on attaining consensus in the scientific community and therefore an accurate critical mass support is required in the whole process. The main goal of this thesis is the development of a Collaborative Environment Tool to assist Evolvable Production Systems consortium in such research efforts and to enhance Evolvable Assembly Systems paradigm dissemination. This work resulted in EASET (Evolvable Assembly Systems Environment Tool), a collaborative environment tool which promotes EAS dissemination and brings forth improvements through the raise of critical mass and collaboration between entities

Intelligent Agents to Support Information Sharing in B2B E-Marketplaces

This article proposes an architecture to support information and knowledge exchange between collaborating business partners. The focus is on knowledge representation and exchange by intelligent agents to support collaborative business functions through agents that exchange problem-specific information in standardized formats. The article then shows the application of the proposed architecture in the context of an infomediary-based B2B E-marketplace

The Role of Models and Communication in the Ad Hoc Multiagent Team Decision Problem

Abstract Ad hoc teams are formed of members who have little or no information regarding one another. In order to achieve a shared goal, agents are tasked with learning the capabilities of their teammates such that they can coordinate effectively. Typically, the capabilities of the agent teammates encountered are constrained by the particular domain specifications. However, for wide application, it is desirable to develop systems that are able to coordinate with general ad hoc agents independent of the choice of domain. We propose examining ad hoc multiagent teamwork from a generalized perspective and discuss existing domains within the context of our framework. Furthermore, we consider how communication of agent intentions can provide a means of reducing teammate model uncertainty at key junctures, requiring an agent to consider its own information deficiencies in order to form communicative acts improving team coordination

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game

In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions due to malfunction or adversarial attack. To address the uncertainty that any agent can be adversarial, we propose a Bayesian Adversarial Robust Dec-POMDP (BARDec-POMDP) framework, which views Byzantine adversaries as nature-dictated types, represented by a separate transition. This allows agents to learn policies grounded on their posterior beliefs about the type of other agents, fostering collaboration with identified allies and minimizing vulnerability to adversarial manipulation. We define the optimal solution to the BARDec-POMDP as an ex post robust Bayesian Markov perfect equilibrium, which we proof to exist and weakly dominates the equilibrium of previous robust MARL approaches. To realize this equilibrium, we put forward a two-timescale actor-critic algorithm with almost sure convergence under specific conditions. Experimentation on matrix games, level-based foraging and StarCraft II indicate that, even under worst-case perturbations, our method successfully acquires intricate micromanagement skills and adaptively aligns with allies, demonstrating resilience against non-oblivious adversaries, random allies, observation-based attacks, and transfer-based attacks