SAJaS: enabling JADE-based simulations

Multi-agent systems (MAS) are widely acknowledged as an appropriate modelling paradigm for distributed and decentralized systems, where a (potentially large) number of agents interact in non-trivial ways. Such interactions are often modelled defining high-level interaction protocols. Open MAS typically benefit from a number of infrastructural components that enable agents to discover their peers at run-time. On the other hand, multi-agent-based simulations (MABS) focus on applying MAS to model complex social systems, typically involving a large agent population. Several MAS development frameworks exist, but they are often not appropriate for MABS; and several MABS frameworks exist, albeit sharing little with the former. While open agent-based applications benefit from adopting development and interaction standards, such as those proposed by FIPA, MABS frameworks typically do not support them. In this paper, a proposal to bridge the gap between MAS simulation and development is presented, including two components. The Simple API for JADE-based Simulations (SAJaS) enhances MABS frameworks with JADE-based features. While empowering MABS modellers with modelling concepts offered by JADE, SAJaS also promotes a quicker development of simulation models for JADE programmers. In fact, the same implementation can, with minor changes, be used as a large scale simulation or as a distributed JADE system. In its current version, SAJaS is used in tandem with the Repast simulation framework. The second component of our proposal consists of a MAS Simulation to Development (MASSim2Dev) tool, which allows the automatic conversion of a SAJaS-based simulation into a JADE MAS, and vice-versa. SAJaS provides, for certain kinds of applications, increased simulation performance. Validation tests demonstrate significant performance gains in using SAJaS with Repast when compared with JADE, and show that the usage of MASSim2Dev preserves the original functionality of the system. © Springer-Verlag Berlin Heidelberg 2015

A Scalable Runtime Platform for Multiagent-Based Simulation

Abstract. Using purely agent-based platforms for any kind of simulation requires to address the following challenges: (1) scalability (efficient scheduling of agent cycles is difficult), (2) efficient memory management (when and which data should be fetched, cached, or written to/from disk), and (3) modelling (no generally accepted meta-models exist: what are essential concepts, what just implementation details?). While dedicated professional simulation tools usually provide rich domain libraries and advanced visualisation techniques, and support the simulation of large scenarios, they do not allow for "agentization" of single components. We are trying to bridge this gap by developing a distributed, scalable runtime platform for multiagent simulation, MASeRaTi, addressing the three problems mentioned above. It allows to plug-in both dedicated simulation tools (for the macro view ) as well as the agentization of certain components of the system (to allow a micro view ). If no agent-related features are used, its performance should be as close as possible to the legacy system used

From simulation to development in MAS a JADE-based approach

Multi-agent systems (MAS) present an effective approach to the efficient development of modular systemscomposed of interacting agents. Several frameworks exist that aid the development of MAS, but they areoften not very appropriate for some kind of uses, such as for Multi-Agent-based Simulation (MABS). Otherframeworks exist for running simulations, sharing little with the former. While open agent-based applicationsbenefit from adopting development and interaction standards, such as those proposed by FIPA, mostMABS frameworks do not support them. In this paper we propose an approach to bridge the gap betweenthe development and simulation of MAS, by putting forward two complementary tools. The Simple API forJADE-based Simulations (SAJaS) enhances MABS frameworks with JADE-based features, and the MAS Simulationto Development (MASSim2Dev) tool allows the automatic conversion of a SAJaS-based simulationinto a JADE MAS, and vice-versa. Repast Simphony was used as the base MABS framework. Our proposalprovides increased simulation performance while enabling JADE programmers to quickly develop their simulationmodels using familiar concepts. Validation tests demonstrate the significant performance gain in usingSAJaS with Repast Simphony when compared with JADE and show that using MASSim2Dev preserves theoriginal functionality of the system

Using of DEVS and MAS Tools for Modeling and Simulation of an Industrial Steam Generator

Complex systems are made of many elementary components in interaction. To model such systems, it is generally more convenient to decompose them into subsystems that are simpler to handle. This new division is to be made in a methodical way, by identification and complete definition of the various structures, actions and interactions of those sub-systems. In this work, the decomposition of the overall system into sub-systems is based primarily on the use of the Discrete EVent System Specification (DEVS) formalism. The obtained atomic and coupled models are formally verified and validated. Then, we use the Multi-agent Development KIT (MAD-KIT) Multi Agent Systems (MAS) operational tools to implement an industrial simulator. This simulator is used by beginner operators in the petroleum field to ameliorate the process of training and learning without stopping the real processes. The advantage of this approach is its adaptability as well as its possibilities of extension (addition of new functionalities). Moreover, the decomposition into sub-systems reduces significantly the complexity of the elements being implemented and therefore, allows a greatmodularity and a better legibility to the system. Our work is realised in collaboration with the production department of natural gas liquefaction (GL1/K Skikda), one of the principal hydrocarbons poles from SONATRACH complex in Algeria

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Multimodal systems have attained increased attention in recent years, which has made possible important improvements in the technologies for recognition, processing, and generation of multimodal information. However, there are still many issues related to multimodality which are not clear, for example, the principles that make it possible to resemble human-human multimodal communication. This chapter focuses on some of the most important challenges that researchers have recently envisioned for future multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable and affective multimodal interfaces

Comparative study of connectionist simulators

This paper presents practical experiences and results we obtained while working with simulators for artificial neural network, i.e. a comparison of the simulators\u27 functionality and performance is described. The selected simulators are free of charge for research and education. The simulators in test were: (a) PlaNet, Version 5.6 from the University of Colorado at Boulder, USA, (b) Pygmalion, Version 2.0, from the Computer Science Department of the University College London, Great Britain, (c) the Rochester Connectionist Simulator (RCS), Version 4.2 from the University of Rochester, NY, USA and (d) the SNNS (Stuttgart Neural Net Simulator), Versions 1.3 and 2.0 from the University of Stuttgart, Germany. The functionality test focusses on special features concerning the establishment and training of connectionist networks as well as facilities of their application. By exemplarily evaluating the simulators\u27 performance, we attempted to establish one and the same type of back-propagation network for optical character recognition (OCR). A respective quality statement is made by comparing the number of cycles needed for training and the recognition rate of the individual simulators

Agent-based modelling – a new method for investigating environmental problems

The agent-based modelling (ABM) represents a newly emerged domain with multiple applications. They started to develop in late 1940’s and they reached maturity in 2000’s – 2010’s. Nowadays, more and more studies are being made using ABM, proving that they are a powerful tool in exploring patterns, interactions and behaviours. As their name suggest, they rely on agents. There is still no formal definition for agents, but they are considered to be autonomous entities, capable of having a certain behaviour and to interact with other agents and/or with the environment they belong to. The environment, the agents and the interactions established form the ABM. Even though this type of models has evolved from mathematics, thanks to computer development, and especially computer graphics, they now offer the possibility to visually explore certain behaviours and to identify a pattern. Also, of great importance and contributing directly to the ABMs application expansion, is the development of a variety of toolkits used for designing agent-based models. Many of the toolkits are open source and continuously implement more simplified programming language. These are the main reasons for which ABMs are used nowadays in a very wide range of domains – from ecology to healthcare and medicine, from archaeology to stock markets, from combats and air traffic control to emergency situations, from social and natural sciences to robotics

Development of emergency response systems by intelligent and integrated approaches for marine oil spill accidents

Oil products play a pervasive role in modern society as one of the dominant energy fuel sources. Marine activities related to oil extraction and transportation play a vital role in resource supply. However, marine oil spills occur due to such human activities or harsh environmental factors. The emergency accidents of spills cause negative impacts on the marine environment, human health, and economic loss. The responses to marine oil spills, especially large-scale spills, are relatively challenging and inefficient due to changing environmental conditions, limited response resources, various unknown or uncertain factors and complex resource allocation processes. The development of previous research mainly focused on single process simulation, prediction, or optimization (e.g., oil trajectory, weathering, or cleanup optimization). There is still a lack of research on comprehensive and integrated emergency responses considering multiple types of simulations, types of resource allocations, stages of accident occurrence to response, and criteria for system optimizations. Optimization algorithms are an important part of system optimization and decision-making. Their performance directly affacts the quality of emergency response systems and operations. Thus, how to improve efficiency of emergency response systems becomes urgent and essential for marine oil spill management. The power and potential of integrating intelligent-based modeling of dynamic processes and system optimization have been recognized to better support oil spill responders with more efficient response decisions and planning tools. Meanwhile, response decision-making combined with human factor analysis can help quantitatively evaluate the impacts of multiple causal factors on the overall processes and operational performance after an accident. To address the challenges and gaps, this dissertation research focused on the development and improvement of new emergency response systems and their applications for marine oil spill response in the following aspects: 1) Realization of coupling dynamic simulation and system optimization for marine oil spill responses - The developed Simulation-Based Multi-Agent Particle Swarm Optimization (SA-PSO) modeling investigated the capacity of agent-based modeling on dynamic simulation of spill fate and response, particle swarm optimization on response allocation with minimal time and multi-agent system on information sharing. 2) Investigation of multi-type resource allocation under a complex simulation condition and improvement of optimization performance - The improved emergency response system was achieved by dynamic resource transportation, oil weathering and response simulations and resource allocation optimization. The enhanced particle swarm optimization (ME-PSO) algorithm performed outstanding convergence performance and low computation cost characteristics integrating multi-agent theory (MA) and evolutionary population dynamics (EPD). 3) Analysis and evaluation of influencing factors of multiple stages of spill accidents based on human factors/errors and multi-criteria decision making - The developed human factors analysis and classification system for marine oil spill accidents (HFACS-OS) framework qualitatively evaluated the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response). The framework was further coupled with quantitative data analysis by Fuzzy-based Technique for Order Preference by Similarity to Idea Solution (Fuzzy-TOPSIS) to enhance decision-making during response operations under multiple criteria. 4) Development of a multi-criteria emergency response system with the enhanced optimization algorithm, multi-mode resource transportation and allocation and a more complex and realistic simulation modelling - The developed multi-criteria emergency response system (MC-ERS) system integrated dynamic process simulations and weighted multi-criteria system optimization. Total response time, response cost and environmental impacts were regarded as multiple optimization goals. An improved weighted sum optimization function was developed to unify the scaling and proportion of different goals. A comparative PSO was also developed with various algorithm-improving methods and the best-performing inertia weight function. The proposed emergency response approaches in studies were examined by oil spill case studies related to the North Atlantic Ocean and Canada circumstances to analyze the modelling performance and evaluate their practicality and applicability. The developed optimization algorithms were tested by benchmarked functions, other optimization algorithms, and an oil spill case. The developed emergency response systems and the contained simulation and optimization algorithms showed the strong capability for decision-making and emergency responses by recommending optimal resource management or evaluations of essential factors. This research was expected to provide time-efficient, and cost-saving emergency response management approaches for handling and managing marine oil spills. The research also improved our knowledge of the significance of human factors/errors to oil spill accidents and response operations and provided improved support tools for decision making. The dissertation research helped fill some important gaps in emergency response research and management practice, especially in marine oil spill response, through an innovative integration of dynamic simulation, resource optimization, human factor analysis, and artificial intelligence methods. The research outcomes can also provide methodological support and valuable references for other fields that require timely and effective decisions, system optimizations, process controls, planning and designs under complicated conditions, uncertainties, and interactions

Proceedings of the KI 2009 Workshop on Complex Cognition

The KI ´09 workshop on Complex Cognition was a joint venture of the Cognition group of the Special Interest Group Artificial Intelligence of the German Computer Science Society (Gesellschaft für Informatik) and the German Cognitive Science Association. Dealing with complexity has become one of the great challenges for modern information societies. To reason and decide, plan and act in complex domains is no longer limited to highly specialized professionals in restricted areas such as medical diagnosis, controlling technical processes, or serious game playing. Complexity has reached everyday life and affects people in such mundane activities as buying a train ticket, investing money, or connecting a home desktop to the internet. Research in cognitive AI can contribute to supporting people navigating through the jungle of everyday reasoning, decision making, planning and acting by providing intelligent support technology. Lessons learned from expert systems research of the nineteen-eighties show that the aim should not be to provide for fully automated systems which can solve specialized tasks autonomously but instead to develop interactive assistant systems where user and system work together by taking advantage of the respective strengths of human and machine. To accomplish a smooth collaboration between humans and intelligent systems, basic research in cognition is a necessary precondition. Insights into cognitive structures and processes underlying successful human reasoning and planning can provide suggestions for algorithm design. Even more important, insights into restrictions and typical errors and misconceptions of the cognitive systems provide information about those parts of a complex task from which the human should be relieved. For successful human-computer interaction in complex domains it has, furthermore, to be decided which information should be presented when, in what way, to the user. We strongly believe that symbolic approaches of AI and psychological research of higher cognition are at the core of success for the endeavor to create intelligent assistant system for complex domains. While insight into the neurological processes of the brain and into the realization of basic processes of perception, attention and senso-motoric coordination are important for the basic understanding of the principles of human intelligence, these processes have a much too fine granularity for the design and realization of interactive systems which must communicate with the user on knowledge level. If human system users are not to be incapacitated by a system, system decisions must be transparent for the user and the system must be able to provide explanations for the reasons of its proposals and recommendations. Therefore, even when some of the underlying algorithms are based on statistical or neuronal approaches, the top-level of such systems must be symbolical and rule-based. The papers presented at this workshop on complex cognition give an inspiring and promising overview of current work in the field which can provide first building stones for our endeavor to create knowledge level intelligent assistant systems for complex domains. The topics cover modelling basic cognitive processes, interfacing subsymbolic and symbolic representations, dealing with continuous time, Bayesian identification of problem solving strategies, linguistically inspired methods for assessing complex cognitive processes and complex domains such as recognition of sketches, predicting changes in stocks, spatial information processing, and coping with critical situations