Biologically-Inspired Concepts for Autonomic Self-Protection in Multiagent Systems

Biologically-inspired autonomous and autonomic systems (AAS) are essentially concerned with creating self-directed and self-managing systems based on metaphors &om nature and the human body, such as the autonomic nervous system. Agent technologies have been identified as a key enabler for engineering autonomy and autonomicity in systems, both in terms of retrofitting into legacy systems and in designing new systems. Handing over responsibility to systems themselves raises concerns for humans with regard to safety and security. This paper reports on the continued investigation into a strand of research on how to engineer self-protection mechanisms into systems to assist in encouraging confidence regarding security when utilizing autonomy and autonomicity. This includes utilizing the apoptosis and quiescence metaphors to potentially provide a self-destruct or self-sleep signal between autonomic agents when needed, and an ALice signal to facilitate self-identification and self-certification between anonymous autonomous agents and systems

Towards Biological Inspiration in the Development of Complex Systems

Greater understanding of biology in modem times has enabled significant breakthroughs in improving healthcare, quality of life, and eliminating many diseases and congenital illnesses. Simultaneously there is a move towards emulating nature and copying many of the wonders uncovered in biology, resulting in "biologically inspired" systems. Significant results have been reported in a wide range of areas, with systems inspired by nature enabling exploration, communication, and advances that were never dreamed possible just a few years ago. We warn, that as in many other fields of endeavor, we should be inspired by nature and biology, not engage in mimicry. We describe some results of biological inspiration that augur promise in terms of improving the safety and security of systems, and in developing self-managing systems, that we hope will ultimately lead to self-governing systems

99% (Biological) Inspiration …

Greater understanding of biology in modern times has enabled significant breakthroughs in improving healthcare, quality of life, and eliminating many diseases and congenital illnesses. Simultaneously there is a move towards emulating nature and copying many of the wonders uncovered in biology, resulting in “biologically inspired” systems. Significant results have been reported in a wide range of areas, with systems inspired by nature enabling exploration, communication, and advances that were never dreamed possible just a few years ago. We warn, that as in many other fields of endeavor, we should be inspired by nature and biology, not engage in mimicry. We describe some results of biological inspiration that augur promise in terms of improving the safety and security of systems, and in developing self-managing systems, that we hope will ultimately lead to self-governing systems.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration: Just a dream?Red de Universidades con Carreras en Informática (RedUNCI

Autonomic Business Processes

Business processes in large organisations are typically poorly understood and complex in structure. Adapting such a business process to changing internal and external conditions requires costly and time consuming investigative work and change management. In contrast autonomic systems are able to adapt to changing environments and continue to function without external intervention. Enabling business processes to adapt to changing conditions in the same way would be extremely valuable. This work investigates the potential to self-heal individual business process executions in generic business processes. Classical and Immune-inspired classification algorithms are tested for their predictive utility with Decision Trees augmented with MetaCost and Immunos 99 exhibiting the best performance respectively. An approach to deriving recovery strategies from historical process data in the absence of a process model is presented and tested for suitability. Also presented is an approach to selecting the best of the determined recovery strategies for application to a business process execution, which is then tested to determine the impact of its parameters on the quality of selected recoveries