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Complex natural systems are natural systems that display strong emergent behavior. Adaptive agents in a resource limited environment optimize their behavior by processing only the most relevant information, i.e., activating the most germane components, learned or evolved from previous experience, thereby responding optimally with least expenditure of energetic resources. A new class of artificial systems known as Resource-constrained Complex Intelligent Dynamical Systems (RCIDS) aim to capture this agent behavior and how these agents switch their attention from one object/subject to the next by way of using activity-aware algorithms in a multi-level hierarchy. In this article, we provide an overview of adaptive systems based on RCIDS. The activity manifested by the resource-constrained agents can be captured towards engineering an intelligent system capable of switching attention. We will discuss the relationship between Activity, Systems Theory, Emergent Behavior, and Second Order Cybernetics, and establish that an activity-based intelligent system (ABIS) can be engineered using Discrete Event Systems (DEVS) formalism through its Levels of Systems Specification. We elaborate on the multi-faceted nature of activity and describe how activity is positioned at a higher level of abstraction than the agent, thereby, abstracting away the agent architecture underneath by making it completely transparent in the same way that Minsky’s Society of Mind abstracts away neurons, axons, and synapses to compose a brain-like system of agents and k-lines. We present the theory behind ABIS, the context-agnostic behavior of algorithms implemented within agents and various issues that need to be addressed to engineer an ABIS.

This article describes an implementation of real-time simulation and control in DEVS-Scheme, a knowledge-based, discrete event environment. A methodology is illustrated in which the plant, its actuators and sensors are described by discrete event models developed within the event-based control paradigm. A model of the controller is employed to validate its design in a plant/actuator/sensor experimental frame.

A high autonomy system needs many models on which to base control, management, design, and other interventions. These models differ in level of abstraction and in formalism. Concepts and tools are needed to organize the models into a coherent whole. The paper deals with the abstraction processes for systematic derivation of related models for use in event-based control. The multifaceted modeling methodology is briefly reviewed. The morphism concepts needed for application to model abstraction are described. A theory for supporting the construction of DEVS models needed for event-based control is then presented. An implemented morphism on the basis of this theory is also described.

Extending discrete event modeling formalisms to facilitate greater symbol manipulation capabilities is important to further their use in intelligent control and design of high autonomy systems. An extension to the DEVS formalism that facilitates symbolic expression of event times by extending the time base from the real numbers to the field of linear polynomials over the reals is defined. A simulation algorithm is developed to generate the branching trajectories resulting from the underlying nondeterminism. To efficiently manage symbolic constraints, a consistency checking algorithm for linear polynomial constraints based on feasibility checking algorithms borrowed from linear programming has been developed. The extended formalism offers a convenient means to conduct multiple, simultaneous explorations of model behaviors. Examples of application are given with concentration on fault model analysis.

A reformulation of the Reiter logic-based theory defines weak and strong diagnoses considering the limitations computer networks receive from these procedures. Results show that only weak diagnoses need to be considered for troubleshooting since these are comparable with minimal strong diagnostic methods. However, information limiting the class of models representing faulty components may accommodate non-minimal strong diagnoses.

We propose to extend the framework for relating information and energy via activity [1]. to the design of hardware. Pifer et. al. [2] present an approach to DEVS-based Hardware Design, Synthesis, and Power Optimization that exploits activity concepts. Here we summarize this approach with emphasis on the role of activity. We also provide some results from [2] that show the utility of various mechanisms to reduce power consumption based on DEV Sand/or activity features.