1,660 research outputs found
Multi-Agent Systems
A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains
Adaptabilni java agenti – alat za programiranje multi-agentskih sistema
The main goal of this thesis is the creation o f the tool agent-oriented programming tool AJA. AJA is the acronym for Adaptable Java Agents. AJA consists o f two programming languages: - A higher-level language used for the description of the main agent parts. This language is called HADL, which is the acronym for Higher Agent Definition Language. - A lower-level language used for the programming o f the agent parts defined in HADL. This language is called Java+. It is actually Java enriched with the constructs for accessing higher-level agent parts defined in HADL. A translator from AJA to Java is implemented in the practical part o f the thesis. AJA agents have the following features: - Agent communicates with other agents using a construct called negotiation. The messages sent can be encrypted or digitally signed in order to ensure the security of the system. - Agent possesses adaptable parameters and neural nets that adapt themselves when the environment changes. - Agent has reflexes, which are the reactive component o f the agent architecture. - Agent can perform its actions parallel. Actions execution is synchronized. - Agent is accessible via Internet, because it acts as a simple HTTP server. People can use this way to communicate with an agent. - Agent has Java Swing based graphical user interface. Its owner uses this interface to communicate with the agent. - Because o f the fact that Java-i- language extends Java, it is possible to use all useful Java features in the implementation o f AJA agents (e.g. JDBC for the database access). The thesis also presents an original approach of integrating artificial intelligence techniques, such as neural nets, with a programming language. Having the artificial intelligence components as a part of the programming language runtime environment makes their use straightforward. A programmer uses the language constructs that are implemented using the artificial intelligence without the need for understanding their background and theory. The thesis contains eight chapters and three appendixes. In the first chapter, an overview of agents and multi-agent systems is given. The second chapter surveys existing agent-oriented programming languages and tools. The third chapter introduces AJA and describes the architecture of AJA agents. The syntax and semantics o f AJA languages HADL and Java+ is described in the fourth chapter. The fifth chapter presents adaptable AJA constructs in more details. To demonstrate and test the created tool, a case-study multi-agent system has been implemented in AJA. There are four personal digital assistant agents in the system. The sixth chapter describes the example agents and positively evaluates the tool. In the seventh chapter the related work and tools are analyzed and compared to AJA. The last chapter concludes the thesis. The first appendix describes the implementation details of the AJA to Java translator. The second appendix is a guide for the installation and usage of the implemented AJA to Java translator. Finally, the third appendix describes step by step how to translate, compile, run, and use the example agents. The thesis contains many references, which include almost all the most important and the most actual papers in the field. The reference list can be found at the end o f the thesis.Glavni doprinos doktorske teze je napravljeni alat za programiranje agenata AJA . AJA - Adaptabilni Java Agenti je jezički alat za programsku implementaciju agenata Sastoji se od dva programska jezika: - Jezik višeg nivoa kojim se opisuju glavne kom ponente agenta. Ovaj jezik se naziva HADL - Higher Agent Definition Language. - Jezik nižeg nivoa koji služi za implementaciju pojedinih komponenti agenta specificiranih HADL jezikom . Ovaj jezik se najava Java+, jer je on zapravo programski jezik Java obogaćen konstrukcijama pomoću kojih je moguće pristupati komponentama agenta, definisanim u jezik u HADL. AJA agent poseduje sledeće osobine: - Sigurna kom unikacija sa drugim A JA agentim a koristeći mehanizam pregovaranja, šifrovanje i digitalno potpisivanje poruka. - Mogućnost adaptiranja na promene u okruženju u kom se nalazi, koristeći neuralne mreže i adaptabilne parametre. - Reaktivnost zasnovana n a kom ponenti zvanoj refleks. - Paralelno izvršavanje akcija agenta u z njihovu internu sinhronizaciju. - D ostupnost agenta preko Interneta. Agent se ponaša kao jednostavan HTTP server. Na ovaj način se drugim osobama omogućuje da komuniciraju sa agentom . - G rafički korisnički interfejs zasnovan n a Java Swing tehnologiji - Pošto se u program iranju agenta koristi Java+, moguće je uposliti sve pogodnosti Jave, kao što su na primer pristup bazama podataka koristeći JDBC , rad sa multimedijalnim sadržajem , itd. U tezi je predstavljen i originalni pristup integrisanja tehnika veštačke inteligencije sa program skim jezikom . U građujući kom ponente veštačke inteligencije u izvršnu okolinu je z ik a čini n jihovo korišćenje veom a jednostavnim . Programer ne mora da bude ekspert iz veštačke inteligencije a da pri tome koristi konstrukcije jezika koje su implementirane pomoću veštačke inteligencije. AJA specifikacija agenta se sastoji od HADL i Java+ delova. U tezi je implementiran prevodioc kojim se A JA specifikacija prevodi u skup klasa programskog jezika Java. Implementiran je i jedan multi-agentski sistem kojim se praktično pokazuje korišćenje i mogućnosti napravljenog alata D oktorska teza sadrži i detaljan pregled oblasti o agentskpj m etodologiji. O n a kruniše višegodišnji rad kandidata i njegovog mentora u ovoj sve značajnijoj oblasti računarstva. Teza sadrži o sam glava i tri dodatka. U prvoj glavi se opisuje oblast agenata i m ulti-agentskih sistem a. Pregled postojećih agentskih program skih jezik a i alata se daje u drugoj glavi. O pis A JA agenata i njihove arhitekture je dat u trećoj glavi teze. Četvrta glava se bavi sintaksom i sem antikom oba A JA jezika: H A D L -a i Jave+. Adaptabilni elem enti A JA agenata se opisuju u petoj glavi. U šestoj glavi je opisan m ulti-agentski sistem koji j e ujed n o i prim er prim ene A JA alata. A JA se sa drugim postojećim agentskim alatim a upoređuje u sedm oj glavi. Osma glava sadrži zaključak. N a kraju se u tri dodatka detaljno opisuju im plem entacija prevodioca A JA -e u Javu, instalacija prevodioca i korišćenje napravljenog m ulti-agentskog sistema respektivno. U doktorskom radu su korišćene i navedene brojne reference kojim a su obuhvaćeni gotovo svi najznačajniji i najaktuelniji radovi iz oblasti multi-agentskih sistema. Lista referenci je navedena na kraju teze
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
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Remote Access to a Prototyping Laboratory
There is a growing global demand for continuing adult higher education particularly in science and engineering subjects. New technologies are emerging which would enable the development of a Remote Access Laboratory for rapid prototyping of Artificial Intelligence, as a learning environment for mechatronic engineering, in which high precision electromechanical devices are designed to exhibit autonomous behaviour.
Secondary research investigated the learning theories for a Remote Access Laboratory, and the current practices for distance learning, involving groupware in shared activity 'collaboratories'. Having determined that the laboratory would need a multi-user interactive environment architecture, with the requirement for adaptability to rapid developments,a distributed software architecture was selected. The laboratory design was subsequently argued to be best served by Intelligent Agents in a Multi-Agent system.
The aims of the research were to establish the viability of a Remote Access Laboratory for mechatronic experimentation, and to evaluate the technologies required to implement such a laboratory environment for rapid prototyping. These were achieved by developing a novel user interface, based on a multi-functional screen layout, and a graphical specification facility to provide robotic navigation that is intuitive to use and does not require text-based programming.
The research investigated the prototyping of robotic behaviour, which used Programming by Demonstration as an innovative technique to prototype robot navigation. The method of designing behaviours met an anticipated need to allow the robot to interact with an environment, to achieve goals under conditions of uncertainty, while requiring a level of abstraction in the behaviour design. The interface structured a composite of the designed behaviours into prototype Artificial Intelligence using a hierarchical behaviour architecture, which complied with the principles of Object Orientated programming. This was subsequently a new and original programming method to facilitate rapid prototyping of Artificial Intelligence design and structuring.
Experimentation involved 20 participants attempting to accomplish a series of tasks which involved using the prototyped interface and an existing text-based robot programming system. The participants were profiled by their formal qualifications, knowledge and experience. The experimental data obtained were used to establish a comparative measure of the prototype interface success compared with an existing distance-learning, home experiment kit, in the form of a small controllable model vehicle. The data obtained provided strong evidence to support the hypothesis that a Programming by Demonstration based system for rapid prototyping is more flexible and easier to use than a previously existing distance learning text-based system. The Programming by Demonstration system showed great promise, being quicker for prototyping, and more intuitive. The learning interface design pioneered new techniques and technologies for rapid prototyping of Artificial Intelligence in a Mechatronics Remote Access Laboratory
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