André Filipe de Moraes Batista, Maria das Graças Bruno Marietto, Wagner Tanaka Botelho, Guiou Kobayashi, Brunno dos Passos Alves, Sidney de Castro and Terry Lima Ruas (April 1st 2011). Principles of Agent-Oriented Programming, Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications, Faisal Alkhateeb, Eslam Al Maghayreh and Iyad Abu Doush, IntechOpen, DOI: 10.5772/14248.

New Zealand

AAMAS 2020

Encapsulating Reacting Behaviour In Goal-Based Plans For Programming BDI Agents

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Interactive Narrative (IN) is an emerging application of planning for control of virtual character behaviours. Despite popularity in research, more widespread adoption of planning has been hindered by the difficulty of authoring planning domain models as INs require models which are robust to dynamic environments and capable of generating a diversity of solutions. To reduce this authoring burden we explored automated extension of partially developed planning domains to address robustness and diversity. We introduce a novel algorithm, THYPE, which proposes additional types of characters and objects to extend a domain model. The extensions increase robustness by enlarging the range of ways to enact planned behaviours. In the paper we embed THYPE within a modular and extensible framework that allows multiple off-line generated extensions to be combined. We present results of a user study that show THYPE suggestions are plausible. We also empirically demonstrate the ability of THYPE extensions to increase the robustness of the models, demonstrate the modular nature of our extension framework by combining THYPE with another extension approach dedicated to recovery from plan failure and present results which show that enhanced performance results from combining multiple extensions


Extending Narrative Planning Domains with Linguistic Resources

In-time Explainability in Multi-Agent Systems: Challenges, Opportunities, and Roadmap

Welcome to EMAS 2020

SARL is a general-purpose agent-oriented programming language. This language aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralisation, reactivity, autonomy and dynamic reconfiguration that are usually considered essential for implementing agent-based applications. Every programming language specifies an execution model. In the case of SARL, this execution model is defined based on the object-oriented paradigm, i.e., the run-time environment is written in Java. Accordingly, and by default, the SARL programs are transformed into their equivalent object-oriented programs written in Java. The goal of this talk is twofold. Firstly, to explain the mapping between the agent-oriented paradigm and the object-oriented paradigm. Secondly, to define transformations from the SARL constructs to the Java constructs. We advocate that these goals enable the SARL developers to understand the SARL statements, and the mapping to the equivalent executable entities.

Model Transformations from the SARL Agent-Oriented Programming Language to an Object-Oriented Programming Language

iLQGames.jl: Rapidly Designing and Solving Differential Games in Julia

Learning Cooperative Solution Concepts from Voting Behavior

ORTHOS: A Trustworthy AI Framework for Data Acquisition

Modern interactive software, such as computer games, employ complex user interfaces. Although these user interfaces make the games attractive and powerful, unfortunately they also make them extremely difficult to test. Not only do we have to deal with their functional complexity, but also the fine grained interactivity of their user interface blows up their interaction space. A space so complex, that traditional automated testing techniques have trouble handling it. An agent-based testing approach offers an alternative solution. The agents' goal driven planning, adaptivity, and reasoning ability can provide an extra edge towards effective navigation in complex interaction space. This paper presents aplib, a Java library for programming intelligent test agents, featuring novel tactical programming as an abstract way to exert control over agents' underlying reasoning-based behavior. This type of control is suitable for programming testing tasks. Aplib is implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Its embedded DSL approach also means that aplib programmers will get all the advantages that Java programmers get: rich language features and a whole array of development tools.

Aplib: Tactical Agents for Testing Computer Games

I briefly motivate the project presented by the paper. The contributions of the paper are outlined. I argue for the use of Blocks World for Teams environment. I briefly describe a transformation of GOAL code to agent logic, and how this leads into proving correctness of agents. Finally, I sketch future steps for the project by identifying key challenges.

A Verification Framework for GOAL Agents

An update on the First Intention Progression Competition.

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Next from AAMAS 2020

Extending Narrative Planning Domains with Linguistic Resources

Similar lecture

Real-time prediction of online shoppers’ purchasing intention using random forest

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads