Frank Cornelissen

A multi-agent system performing one-to-many negotiation for load balancing of electricity use

Abstract Emerging technologies allowing two-way communication between utility companies and their customers are changing the rules of the energy market. Deregulation makes it even more demanding for utility companies to create new business processes for the mutual benefit of the companies and their customers. Dynamic load management of the power grid is essential to make better and more cost-effective use of electricity production capabilities, and to increase customer satisfaction. In this paper, methods from agent technology and knowledge technology have been used to analyse, design, and implement a component-based multi-agent system capable of negotiation for load management. The proof-of-concept prototype system NALM (negotiating agents for load management) developed shows how under certain assumptions peaks in power load can be reduced effectively based on a negotiation process.

Agents negotiating for load balancing of electricity use

Emerging technologies allowing two-way communication between utility companies and their customers, as well as with smart equipment, are changing the rules of the energy market. Deregulation makes it even more demanding for utility companies to create new business processes for mutual benefit. Dynamic load management of the power grid is essential to make better and more cost-effective use of electricity production capabilities, and to increase customer satisfaction. The compositional development method DESIRE has been used to analyse, design, implement and verify a multi-agent system capable of negotiation for load management.

Compositional design and verification of a multi-agent system for one-to-many negotiation

A compositional verification method for multi-agent systems is presented and applied to a multi-agent system for one-to-many negotiation in the domain of load balancing of electricity use. Advantages of the method are that the complexity of the verification process is managed by compositionality, and that parts of the proofs can be reused in relation to reuse of components.

Compositional Verification of Knowledge-Based Systems: A Case Study for Diagnostic Reasoning

When designing complex knowledge-based systems, it is often hard to guarantee that the specification of a system that has been designed actually fulfills the needs, i.e., whether it satisfies the design requirements. Especially for critical applications, for example in aerospace domains, there is a need to prove that the designed system will have certain properties under certain conditions (assumptions). While developing a proof of such properties, the assumptions that define the bounds within which the system will function properly are generated.

Compositional specification and reuse of a generic cooperative agent model

In this paper, one of the informally described models of agent cooperation (Jennings, 1995) has been used to develop and formally specify a generic model of a cooperative agent (GCAM). The compositional development method for multi-agent systems DESIRE supported the principled design of this model of cooperation. To illustrate reusability of the generic model, two application domains have been addressed: collaborative engineering design, and Call Center support.

Compositional Verification of a Multi-Agent System for One-to-Many Negotiation

Verification of multi-agent systems hardly occurs in design practice. One of the difficulties is that required properties for a multi-agent system usually refer to multi-agent behaviour which has nontrivial dynamics. To constrain these multi-agent behavioural dynamics, often a form of organisational structure is used, for example, for negotiating agents, by following strict protocols. The claim is that these negotiation protocols entail a structured process that is manageable with respect to analysis, design and execution of such a multi-agent system. In this paper this is shown by a case study: verification of a multi-agent system for one-to-many negotiation in the domain of load balancing of electricity use. A compositional verification method for multi-agent systems is applied that allows to (1) logically relate dynamic properties of the multi-agent system as a whole to dynamic properties of agents, and (2) logically relate dynamic properties of agents to properties of their subcomponents. Given that properties of these subcomponents can be verified by more standard methods, these logical relationships provide proofs of the dynamic properties of the multi-agent system as a whole.

Compositional verification of knowledge-based task models and problem-solving methods

Abstract.In this paper a compositional verification method for task models and problem-solving methods for knowledge-based systems is introduced. Required properties of a system are formally verified by deriving them from assumptions that themselves are properties of sub-components, which in their turn may be derived from assumptions on sub-sub-components, and so on. The method is based on properties that are formalized in terms of temporal semantics; both static and dynamic properties are covered. The compositional verification method imposes structure on the verification process. Because of the possibility of focusing at one level of abstraction (information and process hiding), compositional verification provides transparency and limits the complexity per level. Since verification proofs are structured in a compositional manner, they can be reused in the event of reuse of models or modification of an existing system. The method is illustrated for a generic model for diagnostic reasoning.

Compositional Verification of Diagnostic Process Models

When designing complex knowledge-based systems, it is often hard to guarantee that the specification of a system that has been designed actually fulfills the needs, i.e., whether it satisfies the design requirements. Especially for critical applications, for example in aerospace domains, there is a need to prove that the designed system will have certain properties under certain conditions (assumptions). While developing a proof of such properties, the assumptions that define the bounds within which the system will function properly are generated.