Biblio

Secure collaboration requires the collaborating parties to apply the
right policies for their interaction.  We adopt a notion of
conditional, directed norms as a way to capture the standards of
correctness for a collaboration.  How can we handle conflicting norms?
We describe an approach based on knowledge of what norm dominates what
norm in what situation.  Our approach adapts answer-set programming to
compute stable sets of norms with respect to their computed conflicts
and dominance.  It assesses agent compliance with respect to those
stable sets.  We demonstrate our approach on a healthcare scenario.

To interact effectively, agents must enter into commitments. What should an agent do when these commitments conflict? We describe Coco, an approach for reasoning about which specific commitments apply to specific parties in light of general types of commitments, specific circumstances, and dominance relations among specific commitments. Coco adapts answer-set programming to identify a maximalsetofnondominatedcommitments. It provides a modeling language and tool geared to support practical applications.

Norms are a promising basis for governance in secure, collaborative environments---systems in which multiple principals interact. Yet, many aspects of norm-governance remain poorly understood, inhibiting adoption in real-life collaborative systems. This work focuses on the combined effects of sanction and observability of the sanctioner in a secure, collaborative environment. We introduce ENGMAS (Exploratory Norm-Governed MultiAgent Simulation), a multiagent simulation of students performing research within a university lab setting. ENGMAS enables us to explore the combined effects of sanction (group or individual) with the sanctioner's variable observability on system resilience and liveness. The simulation consists of agents maintaining ``compliance" to enforce security norms while also remaining ``motivated" as researchers. The results show with lower observability, agents tend not to comply with security policies and have to leave the organization eventually. Group sanction gives the agents more motive to comply with security policies and is a cost-effective approach comparing to individual sanction in terms of sanction costs.  

Norms are a promising basis for governance in secure, collaborative environments---systems in which multiple principals interact. Yet, many aspects of norm-governance remain poorly understood, inhibiting adoption in real-life collaborative systems. This work focuses on the combined effects of sanction and the observability of the sanctioner in a secure, collaborative environment.  We present CARLOS, a multiagent simulation of graduate students performing research within a university lab setting, to explore these phenomena. The simulation consists of agents maintaining ``compliance" to enforced security norms while remaining ``motivated" as researchers. We hypothesize that (1) delayed observability of the environment would lead to greater motivation of agents to complete research tasks than immediate observability and (2) sanctioning a group for a violation would lead to greater compliance to security norms than sanctioning an individual. We find that only the latter hypothesis is supported.  Group sanction is an interesting topic for future research regarding a means for norm-governance which yields significant compliance with enforced security policy at a lower cost. Our ultimate contribution is to apply social simulation as a way to explore environmental properties and policies to evaluate key transitions in outcome, as a basis for guiding further and more demanding empirical research.