Towards time-varying proximal dynamics in Multi-Agent Network Games

Distributed decision making in multi-agent networks has recently attracted significant research attention thanks to its wide applicability, e.g. in the management and optimization of computer networks, power systems, robotic teams, sensor networks and consumer markets. Distributed decision-making problems can be modeled as inter-dependent optimization problems, i.e., multi-agent game-equilibrium seeking problems, where noncooperative agents seek an equilibrium by communicating over a network. To achieve a network equilibrium, the agents may decide to update their decision variables via proximal dynamics, driven by the decision variables of the neighboring agents. In this paper, we provide an operator-theoretic characterization of convergence with a time-invariant communication network. For the time-varying case, we consider adjacency matrices that may switch subject to a dwell time. We illustrate our investigations using a distributed robotic exploration example.Comment: 6 pages, 3 figure

Uutuutta etsivät moniagenttijärjestelmät

This paper considers novelty-seeking multi-agent systems as a step towards more efficient generation of creative artifacts. We describe a simple multi-agent architecture where agents have limited resources and exercise self-criticism, veto power and voting to collectively regulate which artifacts are selected to the domain i.e., the cultural storage of the system. To overcome their individual resource limitations, agents have a limited access to the artifacts already in the domain which they can use to guide their search for novel artifacts. Creating geometric images called spirographs as a case study, we show that novelty-seeking multi-agent systems can be more productive in generating novel artifacts than a single-agent or monolithic system. In particular, veto power is in our case an effective collaborative decision-making strategy for enhancing novelty of domain artifacts, and self-criticism of agents can significantly reduce the collaborative effort in decision making.Peer reviewe

Cooperative Epistemic Multi-Agent Planning for Implicit Coordination

Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Recently, Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. We extend the DEL-based epistemic planning framework to include perspective shifts, allowing us to define new notions of sequential and conditional planning with implicit coordination. With these, it is possible to solve planning tasks with joint goals in a decentralized manner without the agents having to negotiate about and commit to a joint policy at plan time. First we define the central planning notions and sketch the implementation of a planning system built on those notions. Afterwards we provide some case studies in order to evaluate the planner empirically and to show that the concept is useful for multi-agent systems in practice.Comment: In Proceedings M4M9 2017, arXiv:1703.0173

Ensemble case based learning for multi-agent systems

Consultable des del TDXTítol obtingut de la portada digitalitzadaEsta monografía presenta un marco de trabajo para el aprendizaje en un escenario de datos distribuidos y con control descentralizado. Hemos basado nuestro marco de trabajo en Sistemas Multi-Agente (MAS) para poder tener control descentralizado, y en Razonamiento Basado en Casos (CBR), dado que su naturaleza de aprendizaje perezoso lo hacen adecuado para sistemas multi-agentes dinámicos. Además, estamos interesados en agentes autónomos que funcionen como ensembles. Un ensemble de agentes soluciona problemas de la siguiente manera: cada agente individual soluciona el problema actual individualmente y hace su predicción, entonces todas esas predicciones se agregan para formar una predicción global. Así pues, en este trabajo estamos interesados en desarrollar estrategias de aprendizaje basadas en casos y en ensembles para sistemas multi-agente. Concretamente, presentaremos un marco de trabajo llamado Razonamiento Basado en Casos Multi-Agente (MAC), una aproximación al CBR basada en agentes. Cada agente individual en un sistema MAC es capaz de aprender y solucionar problemas individualmente utilizando CBR con su base de casos individual. Además, cada base de casos es propiedad de un agente individual, y cualquier información de dicha base de casos será revelada o compartida únicamente si el agente lo decide así. Por tanto, este marco de trabajo preserva la privacidad de los datos y la autonomía de los agentes para revelar información. Ésta tesis se centra en desarrollar estrategias para que agentes individuales con capacidad de aprender puedan incrementar su rendimiento tanto cuando trabajan individualmente como cuando trabajan como un ensemble. Además, las decisiones en un sistema MAC se toman de manera descentralizada, dado que cada agente tiene autonomía de decisión. Por tanto, las técnicas desarrolladas en este marco de trabajo consiguen un incremento del rendimiento como resultado de decisiones individuales tomadas de manera descentralizada. Concretamente, presentaremos tres tipos de estrategias: estrategias para crear ensembles de agentes, estrategias para realizar retención de casos en sistemas multi-agente, y estrategias para realizar redistribución de casos.This monograph presents a framework for learning in a distributed data scenario with decentralized decision making. We have based our framework in Multi-Agent Systems (MAS) in order to have decentralized decision making, and in Case-Based Reasoning (CBR), since the lazy learning nature of CBR is suitable for dynamic multi-agent systems. Moreover, we are interested in autonomous agents that collaboratively work as ensembles. An ensemble of agents solves problems in the following way: each individual agent solves the problem at hand individually and makes its individual prediction, then all those predictions are aggregated to form a global prediction. Therefore, in this work we are interested in developing ensemble case based learning strategies for multi-agent systems. Specifically, we will present the Multi-Agent Case Based Reasoning (MAC) framework, a multi-agent approach to CBR. Each individual agent in a MAC system is capable of individually learn and solve problems using CBR with an individual case base. Moreover, each case base is owned and managed by an individual agent, and any information is disclosed or shared only if the agent decides so. Thus, this framework preserves the privacy of data, and the autonomy to disclose data. The focus of this thesis is to develop strategies so that individual learning agents improve their performance both individually and as an ensemble. Moreover, decisions in the MAC framework are made in a decentralized way since each individual agent has decision autonomy. Therefore, techniques developed in this framework achieve an improvement of individual and ensemble performance as a result of individual decisions made in a decentralized way. Specifically, we will present three kind of strategies: strategies to form ensembles of agents, strategies to perform case retention in multi-agent systems, and strategies to perform case redistribution

ACoPla: a Multiagent Simulator to Study Individual Strategies in Dynamic Situations

One important issue in multi-agent systems is how to define agents’ interaction strategies in dynamic open environments. Generally, agents’ behaviors, such as being cooperative/altruistic or competitive/adversarial, are defined a priori by their creators. However, this is a weak premise when considering interaction among anonymous self-interested agents. Whenever agents meet, there is always a decision to be made: what is the best group interaction strategy? We argue that the answer depends on the amount of information required to make a decision and on the deadline proximity for accomplishing the task in hand. In certain situations, it is to the agents’ advantage to exchange information with others, while in other situations there are no incentives for them to spend time doing so. Understanding effective behaviors according to the decision- making scenario is still an open issue in multi-agent systems. In this paper, we present a multi-agent simulator (ACoPla) to understand the correlations between agents’ interaction strategy, decision-making context and successful task accomplishment rate. Additionally, we develop a case study in the domain of site evacuation to exemplify our findings. Through this study, we detect the types of conditions under which cooperation becomes the preferred strategy, as the environment changes

Intentional dialogues in multi-agent systems based on ontologies and argumentation

Some areas of application, for example, healthcare, are known to resist the replacement of human operators by fully autonomous systems. It is typically not transparent to users how artificial intelligence systems make decisions or obtain information, making it difficult for users to trust them. To address this issue, we investigate how argumentation theory and ontology techniques can be used together with reasoning about intentions to build complex natural language dialogues to support human decision-making. Based on such an investigation, we propose MAIDS, a framework for developing multi-agent intentional dialogue systems, which can be used in different domains. Our framework is modular so that it can be used in its entirety or just the modules that fulfil the requirements of each system to be developed. Our work also includes the formalisation of a novel dialogue-subdialogue structure with which we can address ontological or theory-of-mind issues and later return to the main subject. As a case study, we have developed a multi-agent system using the MAIDS framework to support healthcare professionals in making decisions on hospital bed allocations. Furthermore, we evaluated this multi-agent system with domain experts using real data from a hospital. The specialists who evaluated our system strongly agree or agree that the dialogues in which they participated fulfil Cohen’s desiderata for task-oriented dialogue systems. Our agents have the ability to explain to the user how they arrived at certain conclusions. Moreover, they have semantic representations as well as representations of the mental state of the dialogue participants, allowing the formulation of coherent justifications expressed in natural language, therefore, easy for human participants to understand. This indicates the potential of the framework introduced in this thesis for the practical development of explainable intelligent systems as well as systems supporting hybrid intelligence

An agent-based intelligent environmental monitoring system

Fairly rapid environmental changes call for continuous surveillance and on-line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated.Comment: Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive Agents, Air-Quality Monitorin