Michael G. Hinchey

Swarms and Swarm Intelligence

Nature-inspired intelligent swarm technologies deals with complex problems that might be impossible to solve using traditional technologies and approaches. Swarm intelligence techniques (note the difference from intelligent swarms) are population-based stochastic methods used in combinatorial optimization problems in which the collective behavior of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. Swarm intelligence represents a meta heuristic approach to solving a variety of problems

Innovative Concepts for Agent-Based Systems

Agent-based approaches to scheduling have gained increasing attention in recent years. One inherent advantage of agent-based approaches is their tendency for robust behavior; since activity is coordinated via local interaction protocols and decision policies, the system is insensitive to unpredictability in the executing environment. At the same time, such “self-scheduling” systems presume that a coherent global behavior will emerge from the local interactions of individual agents, and realizing this behavior remains a difficult problem. We draw on the adaptive behavior of the natural multi-agent system of the wasp colony as inspiration for decentralized mechanisms for coordinating factory operations. We compare the resulting systems to the state-of-the-art for the

Building and implementing policies in autonomous and autonomic systems using MaCMAS

Autonomic Computing, self-management based on high level guidance from humans, is increasingly being accepted as a means forward in designing reliable systems that both hide complexity from the user and control IT management costs. Effectively, AC may be viewed as policy-based self-management. We look at ways of achieving this, with particular focus on agent-oriented software engineering. We propose utilizing MaCMAS, an AOSE methodology for specifying autonomic and autonomous properties of the system independently. Later, by means of composition of these specifications, guided by a policy specification, we construct a specification for the policy and its subsequent deployment. We illustrate this by means of a case study based on a NASA concept mission and describe future work on a support toolkit.

Systems of Systems Verification

To perform new science and exploration, NASA is proposing missions using multispacecraft where each spacecraft can act independently to perform a part of a mission but cannot complete it by itself. These missions are utilizing the concept of “System of Systems” that are being used to develop large systems made up of interacting components, each of which is a system in its own right. To develop these systems with a high level of assurance, new verification methods will be needed to address the added complexity resulting from the nondeterminate nature of these systems as well as emergent behavior. To support the level of assurance that NASA missions require, formal specification techniques and formal verification will play vital roles in the future development of NASA space exploration missions. The role of formal methods will be in the specification and analysis of forthcoming missions, enabling software assurance and proof of correctness of the system of systems behavior, whether or not this behavior is emergent. Formal models derived may also be used as the basis for automating the generation of much of the code for the mission to further reduce the cost and probability of adding new errors during coding.