AGENT AUTONOMY APPROACH TO PROBABILISTIC PHYSICS-OF-FAILURE MODELING OF COMPLEX DYNAMIC SYSTEMS WITH INTERACTING FAILURE MECHANISMS

A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007

Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents\u27 track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified

Giving voice to the Internet by means of conversational agents

Proceedings of: 15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2014), Salmanca, Spain,In this paper we present a proposal to develop conversational agents that avoids the effort of manually defining the dialog strategy for the agent and also takes into account the benefits of using current standards. In our proposal the dialog manager is trained by means of a POMDP-based methodology using a labeled dialog corpus automatically acquired using a user modeling technique. The statistical dialog model automatically selects the next system response. Thus, system developers only need to define a set of files, each including a system prompt and the associated grammar to recognize user responses. We have applied this technique to develop a conversational agent in VoiceXML that provides information for planning a trip.This work has been supported in part by the Spanish Government under i-Support (Intelligent Agent Based Driver Decision Support) Project (TRA2011-29454-C03- 03), and Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02- 02, and CAM CONTEXTS (S2009/TIC-1485

Cognitive Artificial Intelligence: Concept and Applications for Humankind

Computation within the human brain is not possible to be emulated 100% in artificial intelligence machines. Human brain has an awesome mechanism when performing computation with new knowledge as the end result. In this chapter, we will show a new approach for emulating the computation that occurs within the human brain to obtain new knowledge as the time passes and makes the knowledge to become newer. Based on this phenomenon, we have built an intelligent system called the Knowledge-Growing System (KGS). This approach is the basis for designing an agent that has ability to think and act rationally like a human, which is called the cognitive agent. Our cognitive modeling approach has resulted in a model of human information processing and a technique called Arwin-Adang-Aciek-Sembiring (A3S). This brain-inspired method opens a new perspective in AI known as cognitive artificial intelligence (CAI). CAI computation can be applied to various applications, namely: (1) knowledge extraction in an integrated information system, (2) probabilistic cognitive robot and coordination among autonomous agent systems, (3) human health detection, and (4) electrical instrument measurement. CAI provides a wide opportunity to yield various technologies and intelligent instrumentations as well as to encourage the development of cognitive science, which then encourages the intelligent systems approach to human intelligence

Programs Model the Future of Air Traffic Management

Through Small Business Innovation Research (SBIR) contracts with Ames Research Center, Intelligent Automation Inc., based in Rockville, Maryland, advanced specialized software the company had begun developing with U.S. Department of Defense funding. The agent-based infrastructure now allows NASA's Airspace Concept Evaluation System to explore ways of improving the utilization of the National Airspace System (NAS), providing flexible modeling of every part of the NAS down to individual planes, airports, control centers, and even weather. The software has been licensed to a number of aerospace and robotics customers, and has even been used to model the behavior of crowds

Modeling of Dynamic Pricing of Energy for a Smart Grid Using a Multi-agent Framework

The use of smart grids is being promoted to address issues such as energy independence, global warming and emergency resilience. A smart grid is a digitized form of the power grid and is comprised of an intelligent monitoring system that keeps track of the two-way digital communications in the system. A multi-agent system is a collection of interacting intelligent agents that can be used in problem solving for systems that are difficult or impossible to be solved by an individual agent. Applications of multi-agent systems can range from transportation, logistics, graphics, networking and mobile technologies to modeling real world scenarios to achieve automatic and dynamic load balancing, pricing, and disaster response. The goal of this project was to design and implement a multi-agent system to model dynamic pricing of electricity in a smart grid, thereby improving the overall efficiency of electricity consumption in a real world scenario. This project was accomplished by devising and implementing a multi-agent system for regulating automatic and dynamic pricing of electricity by monitoring power consumption periods and rising or falling prices accordingly. The system developed has the capability of rising and lowering the prices of electricity based on the availability of electricity from energy sources. This system will depict how much energy the consumers are using and how much it is actually costing them. We believe that the logistics analyzed above will help energy-consumption utilities and consumers to make better energy-efficient decisions

A Multi-Agent Approach Towards Collaborative Supply Chain Management

Supply chain collaboration has become a critical success factor for supply chain management and effectively improves the performance of organizations in various industries. Supply chain collaboration builds on information sharing, collaborative planning and execution. Information technology is an important enabler of collaborative supply chain management. Many information systems have been developed for supply chain management from legacy systems and enterprise resource planning (ERP) into the newly developed advanced planning and scheduling system (APS) and e-commerce solutions. However, these systems do not provide sufficient support to achieve collaborative supply chain. Recently, intelligent agent technology and multi-agent system (MAS) have received a great potential in supporting transparency in information flows of business networks and modeling of the dynamic supply chain for collaborative supply chain planning and execution. This paper explores the similarities between multi-agent system and supply chain system to justify the use of multi-agent technology as an appropriate approach to support supply chain collaboration. In addition, the framework of the multi-agent-based collaborative supply chain management system will be presented