Gerrit Anders

Constraining Self-organisation Through Corridors of Correct Behaviour: The Restore Invariant Approach

Self-organisation aspects and the large number of entities in Organic Computing (OC) systems make them extremely hard to predict and analyse. However, the application of OC principles to, e.g., safety critical systems, is usually not conceivable without behavioural guarantees. In this article, a rigorous approach called the Restore Invariant Approach is presented, which provides a specification paradigm and a formal framework that allows to give guarantees for a system despite of self-organisation. The approach provides a method for specifying unwanted system states by constraining the system and defining a corridor of correct behaviour. Furthermore, a decentralised algorithm for monitoring and restoring the invariant based on coalition formation is presented.

On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty

Multi-agent systems often consist of heterogeneous agents with different capabilities and objectives. While some agents might try to maximize their systems utility, others might be self-interested and thus only act for their own good. However, because of their limited capabilities and resources, it is often necessary that agents cooperate to be able to satisfy given tasks. To work together on such a task, the agents have to solve a task allocation problem, e.g., by teaming up in groups like coalitions or distributing the task among themselves on electronic markets. In this paper, we introduce two algorithms that allow agents to cooperatively solve a dynamic task allocation problem in uncertain environments. Based on these algorithms, we investigate the influence of inter-agent variation on the systems behavior. One of these algorithms explicitly exploits inter-agent variation to solve the task without communication between the agents, while the other builds upon a fixed overlay network in which agents exchange information. Throughout the paper, the frequency stabilization problem from the domain of decentralized power management serves as a running example to illustrate our algorithms and results.

A Decentralized Multi-agent Algorithm for the Set Partitioning Problem

A decentralized algorithm for solving set partitioning problems (SPP) has numerous applications in multi-agent systems. Apart from its relevance for problems of operations research, the SPP is equivalent to clustering of agents as well as the generation of coalition structures. SPADA, the algorithm proposed in this paper, does not use central metrics, relies exclusively on local agent knowledge, and respects agent autonomy. By trying to increase its own benefit, each subset of agents, representing a coalition or cluster, contributes to establishing a suitable partitioning. Agents are at liberty to decide if they want to join or leave a subset. All operations that change the composition of these subsets or the acquaintances of agents can be mapped onto a graph that represents these relations. The algorithm is evaluated in a scenario of decentralized energy management where it is used as a self-organization mechanism. The evaluations show that the quality of the partitioning is within 10% of the solutions found by a particle swarm optimizer with global knowledge.

Cooperative Resource Allocation in Open Systems of Systems

Resource allocation is a common problem in many technical systems. In multi-agent systems, the decentralized or regionalized solution of this problem usually requires the agents to cooperate due to their limited resources and knowledge. At the same time, if these systems are of large scale, scalability issues can be addressed by a self-organizing hierarchical system structure that enables problem decomposition and compartmentalization. In open systems, various uncertainties—introduced by the environment as well as the agents’ possibly self-interested or even malicious behavior—have to be taken into account to be able to allocate the resources according to the actual demand. In this article, we present a trust- and cooperation-based algorithm that solves a dynamic resource allocation problem in open systems of systems. To measure and deal with uncertainties imposed by the environment and the agents at runtime, the algorithm uses the social concept of trust. In a hierarchical setting, we additionally show how agents create constraint models by learning the capabilities of subordinate agents if these are not able or willing to disclose this information. Throughout the article, the creation of power plant schedules in decentralized autonomous power management systems serves as a running example.

An Approach for Isolated Testing of Self-Organization Algorithms

We provide a systematic approach for testing self-organization (SO) algorithms. The main challenges for such a testing domain are the strongly ramified state space, the possible error masking, the interleaving of mechanisms, and the oracle problem resulting from the main characteristics of SO algorithms: their inherent non-deterministic behavior on the one hand, and their dynamic environment on the other. A key to success for our SO algorithm testing framework is automation, since it is rarely possible to cope with the ramified state space manually. The test automation is based on a model-based testing approach where probabilistic environment profiles are used to derive test cases that are performed and evaluated on isolated SO algorithms. Besides isolation, we are able to achieve representative test results with respect to a specific application. For illustration purposes, we apply the concepts of our framework to partitioning-based SO algorithms and provide an evaluation in the context of an existing smart-grid application.

Partial Valuation Structures for Qualitative Soft Constraints

Soft constraints have proved to be a versatile tool for the specification and implementation of decision making in adaptive systems. A plethora of formalisms have been devised to capture different notions of preference. Wirsing et al. have proposed partial valuation structures as a unifying algebraic structure for several soft constraint formalisms, including quantitative and qualitative ones, which, in particular, supports lexicographic products in a broad range of cases. We demonstrate the versatility of partial valuation structures by integrating the qualitative formalism of constraint relationships as well as the hybrid concept of constraint hierarchies. The latter inherently relies on lexicographic combinations, but it turns out that not all can be covered directly by partial valuation structures. We therefore investigate a notion for simulating partial valuation structures not amenable to lexicographic combinations by better suited ones. The concepts are illustrated by a case study in decentralized energy management.

Patterns to Measure and Utilize Trust in Multi-agent Systems

This paper introduces three patterns to measure and utilize trust values in multi-agent systems (MAS). They allow a software engineer to incorporate mechanisms to gauge the benefit of interactions with other agents into the modeling and design process. In particular, patterns for trust from direct observation, reputation, and Trusted Communities are described. Each pattern contains the elements necessary to incorporate trust into a MAS and the collaboration required between the elements to make use of the measured values. The application of the patterns is demonstrated with an example from the domain of energy management systems.

An Effective Implementation of Norms in Trust-Aware Open Self-Organising Systems

We discuss the implementation of a normative system in an open self-organising system, including an OCL-based format for norms we settled on, the design of the feedback loops for their observation and adaptation, as well as a corresponding software architecture. These elements allow designers to quickly integrate a normative sub-system in a MAS and to define norms based on existing design concepts.

A Heuristic for Constrained Set Partitioning in the Light of Heterogeneous Objectives

The set partitioning problem (SPP) is at the heart of the formation of several organizational structures in multi-agent systems. Essentially, such structures can improve scalability and enable cooperation between agents with limited resources and capabilities. We present a discrete Particle Swarm Optimizer that solves the NP-hard SPP in the presence of partitioning constraints which restrict valid partitionings in terms of acceptable ranges for the number and the size of partitions. To be applicable to a broad range of applications, our algorithm relies on basic set operations to come to a solution and is thus independent of the characteristics of a specific objective function. Among other things, it can be used for coalition structure generation, strict partitioning clustering, anticlustering, and, combined with an additional control loop, even for the creation of hierarchical partitionings. Our evaluation confirms that it finds high-quality solutions in different scenarios and for various objectives in short time.

The Social Concept of Trust as Enabler for Robustness in Open Self-Organising Systems

The participants in open self-organising systems, including users and autonomous agents, operate in a highly uncertain environment in which the agents’ benevolence cannot be assumed. One way to address this challenge is to use computational trust. By extending the notion of trust as a qualifier of relationships between agents and incorporating trust into the agents’ decisions, they can cope with uncertainties stemming from unintentional as well as intentional misbehaviour. As a consequence, the system’s robustness and efficiency increases. In this context, we show how an extended notion of trust can be used in the formation of system structures, algorithmically to mitigate uncertainties in task and resource allocation, and as a sanctioning and incentive mechanism. Beyond that, we outline how the users’ trust in a self-organising system can be increased, which is decisive for the acceptance of these systems.