Quantum computing is a near-term technology that through optimization will dramatically increase overall computing power. Intelligent Automation is a branch of Artificial Intelligence that streamlines complex tasks, often currently done by humans, that adapts to internal and external environmental factors. Leveraging quantum computing’s ability to optimize for performance, scheduling, routing, and resource allocation orchestrated with agent-based technologies that divide large problems into smaller one produces systems that save time in a simple way. By extending the reach of agents into the physical work through agent-aware sensors and controls, solutions that bridge the physical world with the quantum world become a reality.

Agent-based solutions reduce complex applications into manageable components that run independently of each other. Quantum computing solves particularly difficult problems, such as Constraint Satisfaction Problems, that are time consuming on classical computers. Segmenting a complex problem into quantum and non-quantum components is well-suited to agent-based processing since the framework is already in place to simplify and solve through a divide-and-conquer approach. In this paper, we show how Quantum Agents in an agent-based intelligent system can be an ideal interface to quantum computing for solving many common and difficult to solve real-world problems. Quantum computing is being used for today's applications for optimization, resource allocation, scheduling and route calculations. Pairing quantum computing's advantages with the flexibility, integrity, and reusability of an agent-based system, produces application solutions quickly, that are reliable and evolve to meet expanding needs and growing scale.

Multi-agent autonomous systems solve many problems by organizing processing actions around triggering events. Agents working independently of one another, target their own specific criteria based on data, time, location or any combination of these. Responses to triggered events result in new or updated data, notifications or actions upon processes. Virtualizing the processing environment of agents decouples computing requirements and resources from specific agent functions to provide better resource management, expanded agent actions, and more sophisticated adaptation algorithms, included those based on machine leaning (ML) and deep learning (DL). Applications based on containerized autonomous agents where ML and DL based adaptations, and computing resources managed by container orchestration, are more dynamic, responsive and adaptive, where agents can number in the tens or hundreds of thousands or more. Structured data from unstructured data sources are the basis for cognitive and Internet of Things (IoT) applications. Intelligent systems with containerized autonomous agents are well suited to these types of applications. This paper describes the theory and applied real-world use of containerized autonomous agents in intelligent systems.

Applications and Operating Systems are programs responding to stimuli to perform tasks on computers, and these programs can be constructed in ways to minimize overall complexity of the software lifecycle, by organizing tasks into units that bring about a response and adaptation to stimuli. The response is the task's main function, the stimuli are user interaction, data source conditions, or time-based events, and adaptation is an opportunity for the task to adapt to its environment. Developing software using autonomous adaptive agents in an intelligent system minimizes overall complexity by organizing computing into reusable, scalable logical units that are by nature parallelizable. Each autonomous agent has a common core of virus resistant outer virtual casing by continually comparing its core attributes against master copies distributed via block-chain technology. Building upon research and implementation of these concepts of using intelligent systems using autonomous block-chain protected agents to rapidly create commercial-grade software over a wide variety of domains, we discuss in this paper how these concepts not only facilitate application development but can also be used for operating systems.

Components to a robust, secure, reliable and dynamic platform for the Internet of Things includes anticipating for change, and preparing for the unexpected. IoT intends to deliver the next wave of disruption and growth to every facet of digital life, and now is the time for laying the groundwork, upon which IoT processes can be built. In this paper, we present the culmination of over 15 years of Artificial Intelligence research, software development and deployment of commercial applications built using adaptive software agents in an intelligent system framework with automatic anomaly detection and notification. We have created a system of components that have allowed us to create complex applications quickly through the use and re-use of adaptive agents in an intelligent system for automation, communication, and control. To be secure and adaptive, agents were also created whose primary purpose is to monitor normal behavior and react when something out of the ordinary occurs. Reactions can include halting processes, starting up other processes, notifying humans, interacting with 3rd party systems, etc. Each agent includes its own security layer as part of the agent triggering mechanism, as well as the ability to communicate thru all digital channels including Push Notification. Push Notification allows for server-based communication to any number of IoT devices and does not rely upon SIM-based (phone, text messaging) nor IP-based (networking) communication channels.

In this paper, we present a process, developed over years of practical commercial use, where applications accomplishing a wide variety of complex tasks are created from a common framework, through the use, recombination and iterative refinement of autonomous agents in a Multi-Agent Intelligent System. Driven by a need to solve real-world problems, our focus is to make businesses run more efficiently in an increasingly complex world of systems and software that must work together seamlessly. By listening closely to our customers’ problems, we discovered points of commonality, as well as patterns of anomalies related to the flow of data through communication channels and data processing systems, include accounting, inventory, customer relationship management, scheduling systems and many more. We solved their problems through the creation of an Intelligent System, where we defined and implemented software agents that were highly configurable, responsive in real-time and useable in various settings. Autonomous agents adhere to a standard format of three major components: the goal or triggering criteria, the action, and the adaptation response. Agents run within a common Intelligent System framework and agent libraries provide a vast set of component behaviors to build applications from. Agents have one or more of the following component behaviors: sensory aware, geo-position aware, temporally aware, API aware, device aware, and many more. Additionally, there are manager-level agents whose goal is to keep the overall system in balance, through dynamic resource allocation on a system level. To prove the viability of this process, we present a variety of applications representing wide ranging behaviors, many with overlapping agents, created via this approach, all of which are in active commercial use. Finally, we discuss future enhancements toward self-organization, where end users express their requirements declaratively to solve larger business needs, resulting in the automatic instantiation of a solution specific intelligent system.

The platform- and language-agnostic nature of XML makes it a natural choice for developers building cross-platform applications and is rapidly becoming the mechanism of choice for application designers who need to store and share data. Simply knowing the syntax and features of the language just isn't enough if you want to build powerful and efficient XML driven applications. You need to understand how to use the language and its features in the most effective manner. This is where this book comes in - assuming familiarity with the mechanics of XML, it analyzes all of the critical pieces of the XML space that require careful design in order to build efficient, robust, and extensible applications.