A Model of Trust, Moods, and Emotions in Multiagent Systems and its Empirical Evaluation

Abstract We study the interplay of moods, emotions, and trust in decisionmaking contexts characterized by commitments among agents. We develop a general approach representing the relationships among these concepts via a Bayesian network model. Our approach incorporates insights from the literature and provides a computational methodology for identifying improved Bayesian models. Based on observations from an empirical study, we motivate a refined Bayesian model involving the above-mentioned concepts that goes beyond the relationships known in the literature. Our findings include (1) the violation of a commitment affects trust more than its satisfaction; (2) goal satisfaction affects mood and emotion more than commitment satisfaction, but the outcome of a commitment affects trust more than the outcome of a goal; and (3) an agent's prior mood and trust affect whether it satisfies its commitments

Protos: A Cross-Organizational Business Modeling Tool (Demonstration)

Traditional approaches to cross-organizational business modeling use low-level abstractions such as data and control flow. These approaches result in rigid models that over-constrain business execution. Further, because such approaches ignore the underlying business relationships that drive process execution, they lack the notion of business level correctness. Telang and Singh [5] propose a high-level business modeling approach based upon (social) commitments to address these shortcomings. The high-level model captures the business relationships in terms of commitments between the participants. Telang and Singh [5] develop a method for verifying if a low-level interaction model satisfies a high-level business model. They propose a top-down methodology in which a Business analyst first develops a high-level business model. An IT analyst then develops UML 2.0 sequence diagrams, and verifies if they satisfy the high-level model. Protos is an Eclipse-based tool that implements Telang and Singh’s [5] methodology. It enables: (a) the development of a high-level business model using reusable patterns, (b) the development of UML 2.0 sequence diagrams, as a low-level operational representation, and (c) the automated verification of the UML 2.0 sequence diagrams with respect to the high-level business model

Monitoring Commitments in People-Driven Service Engagements

Abstract—People-driven service engagements involve communication over channels such as chat and email. Such engagements should be understood at the level of the commitments that the participants create and manipulate. Doing so provides a grounding for the communications and yields a business-level accounting of the progress of a service engagement. Existing work on commitment-based service engagements is limited to designtime model creation and verification. In contrast, we present a novel approach for capturing commitment-based engagements that are created dynamically in conversations. We monitor commitments identifying their creation, delegation, completion, or cancellation in the conversations. We have developed a prototype and evaluated it on real-world chat and email datasets. Our prototype captures commitments with a high F-measure of 90 % in emails (Enron email corpus) and 80 % in chats (HP IT support chat dataset) and provides promising results for capturing additional commitment operations. I

HL7 messages applicable to the breast cancer diagnosis scenario.

<p>HL7 messages applicable to the breast cancer diagnosis scenario.</p

Flexibility (total number of SDs and alt, opt, par fragments) and objective quality (total number of missing guards and incorrect SDs) as assessed by experts.

<p>Flexibility (total number of SDs and alt, opt, par fragments) and objective quality (total number of missing guards and incorrect SDs) as assessed by experts.</p

Comma methodology steps.

<p>Comma methodology steps.</p

A Comma business model for the ASPE scenario.

<p>A Comma business model for the ASPE scenario.</p