Abstract

Multi-Agent Systems model how different autonomous Agents, with limited knowledge, interact with each other in a shared Environment. A usual use case is to use Agents as a Team and give them a goal, which can only be achieved by multiple agents.

There are is a large number of Parameters that the operator of those teams needs to set in order for the team to interact successfully and reach its goal. Therefore, Methods, from the machine-learning domain, are used to automatically explore all possible parameters and find the most optimal ones.

This Survey will concentrate on Research where the Team Size is greater than two or three. At first, I present the history and background of Multi-Agent-Systems. After that, I give an overview of the  of the Problems, which arise when large teams and machine-learning are used together. After that, I show existing algorithms, which can cope with those problems. Finally, I discuss what further research can be done in the area.

Punctuation Homework

  • We live in the era of Big Data with storage and transmission capacity measured not just in terabytes but in petabytes (where peta- denotes a quadrillion or a thousand trillion). Data collection is constant and even insidious, with every click and every “like” stored somewhere for something. This book reminds us that data is anything but “raw” that we shouldn’t think of data as a natural resource, but as a cultural one that needs to be generated protected and interpreted. The book’s essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as: the ways that different kinds of data and different domains of inquiry are mutually defining how data are variously “cooked” in the processes of their collection and use and conflicts over what can or can’t be “reduced” to data. Contributors discuss the intellectual history of data as a concept, describe early financial modeling and some unusual sources for astronomical data, discover the prehistory of the database in newspaper clippings and index cards, and consider contemporary “dataveillance” of our online habits as well as the complexity of scientific data curation.
  • During succession, ecosystem development occurs but in the long term absence of catastrophic disturbance a decline phase eventually follows. We studied six long term chronosequences in Australia, Sweden, Alaska, Hawaii, and New Zealand; for each the decline phase was associated with a reduction in tree basal area and an increase in the substrate nitrogen to phosphorus ratio, indicating increasing phosphorus limitation over time. These changes were often associated with reductions in litter decomposition rates, phosphorus release from litter and biomass, and activity of decomposer microbes. Our findings suggest that the maximal biomass phase reached during succession cannot be maintained in the long term absence of major disturbance and that similar patterns of decline occur in forested ecosystems spanning the tropical temperate and boreal zones.
  • Facebook’s Graph API is an API for accessing objects and connections in Facebook’s social graph. To give some idea of the enormity of the social graph underlying Facebook it was recently announced that Facebook has 901 million users and the social graph consists of many types beyond just users. Until recently, the Graph API provided data to applications in only a JSON format. In 2011, an effort was undertaken to provide the same data in a semantically enriched RDF format, containing Linked Data URIs. This was achieved by implementing a flexible and robust translation of the JSON output to a Turtle output. This paper describes the associated design decisions, the resulting Linked Data for objects in the social graph, and known issues.

Research Homework

I decided to narrow down my original topic of “Multi-Agent Systems” to something like “How can a Team of Agents learn to achieve a goal cooperatively”.

  1. Shoham, Y., & Leyton-brown, K. (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. ReVision, 54(1-4), 513 p. – The Foundations are important, it also gives a good overview of the whole field.
  2. Stone, P., & Veloso, M. M. (2000). Multiagent Systems: A Survey from a Machine Learning Perspective. Autonomous Robots, 8(3), 345-383. –  Survey on how Machine Learning is used in Multi-Agent Systems in general.
  3. Panait. (2005). Cooperative Multi-Agent Learning: The State of the Art. Autonomous Agents and Multi-Agent Systems, 11, 387-434. – Survey on how a team of agents can learn to cooperate to achieve a goal.
  4. Byrski, A., Dreżewski, R., Siwik, L., & Kisiel-Dorohinicki, M. (2015). Evolutionary multi-agent systems. The Knowledge Engineering Review, 30(02), 171-186.  – Describes an Evolutionary Approach to Multi-Agent Learning.
  5. Buşoniu, L., Babuška, R., & De Schutter, B. (2008). A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews. – Describes how to use RL to teach a team of Agents.