Multiparty agreements often arise in a multiagent system where autonomous agents interact with each other to achieve a global goal. Multiparty agreements are traditionally represented by messaging protocols or event- condition-action rule sets in which agents exchange messages in a predefined sequence to ensure both global and local consistencies. However, these models do not readily incorporate agents' autonomy and heterogeneity, which limits their ability to help build a flexible open system. Commitments have been studied for modelling various agent interactions. This paper introduces commitments as the key elements in formulating multiparty agreements. Our model focuses on how agents may negotiate with each other to build a mutual agreement based on their individual constraints. The actual execution sequence is validated by checking the compliance of commitment casual relations. Our approach is geared toward constructing business processes where agents are mutually constrained in a manner that preserves their autonomy and heterogeneity.

Today's business process management faces challenges in dealing with business abnormalities and ever-changing business requirements. Traditional business process management approaches evolved from software engineering and workflow management where activities, messages and control logic are given prominence. The resulting models specify low-level details of execution and coordination. Difficulties are encountered when modelling long-lived business transactions involving information updates and execution exceptions. To handle such situations, current approaches would implement excessive activities without suitable abstractions, thereby arbitrarily fragmenting the business requirements. We propose a commitment-based approach for business process modelling that formulates business processes as multiagent systems. Organizational structure and its effect on interactions are described using commitments and causality. Agents act as process executors and maintain the commitments made to each other. Updates and exceptions yield commitment operations under which processes are updated and reexecuted. Our approach brings commitment semantics into business modelling and enables agent collaboration for business process enactment. In this research, we derive commitment protocols from agent conversations and generate agent execution models. We also formalize our approach using the π-calculus and prove its correctness. To demonstrate the practical use of our approach, we formalize multiparty agreements with commitments and present algorithms on how to detect agreement conflicts and build satisfiable commitment sets.

Web services are gaining popularity for supporting reusable business processes across distributed and heterogeneous environments. Current Web services are understood as taking inputs, executing their internal logic, and delivering outputs. When outputs are delivered, the interaction related to the given service ends. However, in many cases, the delivery of a service does not mean that the business transaction has ended, because there may be a change or cancellation of the original request (from the service requester) or an update of results (from the service provider). Current approaches deal with such scenarios by creating additional operations, thereby arbitrarily splitting the business logic and thus complicating service modeling and execution. This paper introduces the persistence of services via commitments. Commitments represent agreements between service requesters and providers. The commitments must be fulfilled to ensure successful business transactions. A commitment may last longer than individual episodes of service request and delivery. Operations on commitments, such as create, update, cancel, and discharge, reflect the persistence of the corresponding services and assist in constructing service models and protocols. We show that commitment-enhanced Web service descriptions are simpler and yet more general than current approaches, so that reliable and flexible service compositions can be produced.

Multiparty agreements often arise in a multiagent system where autonomous agents interact with each other to achieve a global goal. Multiparty agreements are traditionally represented by messaging protocols or event-condition-action rule sets in which agents exchange messages in a predefined sequence to ensure both global and local consistency. However, these models do not readily incorporate agents' autonomy and heterogeneity, which limits their ability to help build a flexible open system. Commitments have been studied for modelling various agent interactions. They have also been used as the key elements for formulating multi-party agreements and centralized approaches for resolving potential conflicts. This paper extends the above results by refining the formalizations and the existing protocols and proposing a decentralized protocol which is more efficient in resolving conflicts. It also introduces the concept of protocol safety, which ensures that agents not only interact efficiently but also correctly. This approach is geared toward constructing business processes where agents are mutually constraints in a manner that preserves their autonomy and heterogeneity.

Commitments among agents can be used to model business processes in a manner that preserves the autonomy and heterogeneity of the interacting parties. Dooley graphs are widely used in conversation-based multiagent system design. Commitments and their causal relationships can be inferred from Dooley graphs to flexibly model business processes. We present a theoretical foundation for Dooley graphs, commitments, and causality in terms of the ¿-calculus, a process algebra for specifying concurrent systems, which has found application in business process modeling. This paper expresses the key elements of Dooley graphs (roles, characters, conversations) and of our approach (commitments and causal diagrams) using the ¿-calculus. Further, we accommodate a new variety of primitives for exception handling termed reentrant connectors. The ¿-calculus enables us to derive useful properties from a given model and to validate its correctness. We show how these properties assist in building an entire business process model in which agents interact flexibly.

This paper unifies two recent strands of research in multiagent system design. One, commitments are widely recognized as capturing important aspects of interactions among agents, but current approaches tend to emphasize individual commitments and typically restrict themselves to interactions between pairs of agents. Two, methodologies for multiagent system design consider protocols and coordination requirements, but do not seriously accommodate commitments. This paper proposes a methodology to infer commitments from an example conversation among several parties. Based on the conversation, we build a commitment causality diagram indicating the causal relations among the commitments. Using this diagram, we generate behavior models for each role. We show that the models produced successfully capture commitment-level protocols and allow flexible implementation of non-commitment communications provided the causal relations are preserved.

Successful e-commerce presupposes techniques by which autonomous trading entities can interoperate. Although much progress has been made on data exchange and payment protocols, interoperation in the face of autonomy is still inadequately understood. Current techniques, designed for closed environments, support only the simplest interactions. We develop a multiagent approach for interoperation in e-commerce. This approach consists of (1) a behavioral model to specify autonomous, heterogeneous agents representing different trading entities (businesses, consumers, brokers), (2) a metamodel that provides a language (based on XML) for specifying a variety of service agreements and accommodating exceptions and revisions, and (3) an execution architecture that supports persistent and dynamic (re)execution.

Successful e-commerce presupposes techniques by which autonomous trading entities can interoperate. Although progress has been made on data exchange and payment protocols, interoperation in the face of autonomy is still inadequately understood. Current techniques, designed for closed environments, support only the simplest interactions. We develop a multiagent approach for interoperation of business process in e-commerce. This approach consists of (1) a behavioral model to specify autonomous, heterogenous agents representing different trading entities (businesses, consumers, brokers), (2) a metamodel that provides a language (based on XML) for specifying a variety of service agreements and accommodating exceptions and revisions, and (3) an execution architecture that supports persistent and dynamic (re)execution.

Successful e-commerce presupposes techniques by which autonomous trading entities can interoperate. Although progress has been made on data exchange and payment protocols, interoperation in the face of autonomy is still inadequately understood. Current techniques, designed for closed environments, support only the simplest interactions. We develop a multiagent approach for interoperation of business process in e-commerce. This approach consists of (1) a behavioral model to specify autonomous, heterogeneous agents representing different trading entities (businesses, consumers, brokers), (2) a metamodel that provides a language (based on XML) for specifying a variety of service agreements and accommodating exceptions and revisions, and (3) an execution architecture that supports persistent and dynamic (re)execution. key words: commitments, statecharts, business process, interoperation, e-commerce

The PEPA Workbench is a modelling tool for composing and checking descriptions of systems composed of communicating concurrently active components. It implements the PEPA language and provides solution and analysis facilities for PEPA models. The PEPA Workbench exists in several different versions and this project has extended and adapted a version which re-implemented the original Standard ML version in Java and Pizza, a language which extends Java with additional data structures. One goal of this project is to enhance the user interface of the Java version by making use of Java 1.2’s Swing API. A necessary addendum to this would be re-implementing the Pizza classes as pure Java classes. Another goal is to extend the PEPA Workbench with facilities to compute transient solutions which solve probability distributions at an arbitrary time t.

We consider semantic exceptions, which arise when a task yields results that are incorrect, inconsistent with related tasks, or incomplete. Semantic exceptions are especially prominent in the large, i.e., when we construct and execute a workflow. A workflow is a composite computation with several interoperating components and interacting processes. Detecting and resolving semantic exceptions is critical to the functioning of a workflow, especially when its member activities are autonomous, heterogeneous, long-lived, and interact in subtle ways. Unfortunately, present workflow techniques offer little support for exceptions. For modeling, they provide few abstractions beyond activity charts. For enactment, they are not flexible enough to allow a component to detect and resolve semantic exceptions properly. We describe a multiagent approach for handling semantic exceptions. Our approach is based on high-level abstractions such as commitments, a process metamodel that accommodates commitments and allied concepts, a behavior model to specify agents, and an execution architecture that handles persistent and dynamic (re)execution. We can handle a variety of semantic exceptions by formulating a number of commitment patterns that cover the important situations. The behavior model and the commitment patterns are expressed as statecharts and executed in a rule-based system. In this way, advanced abstractions based on multiagent systems are mapped into conventional software techniques. Topic: Models and paradigms This research has been supported by the U.S. National Science Foundation under grants IIS-9529179 and IIS9624425, and by IBM Corporation.