Alexandra Coman

Identifying Connectors and Communities: Understanding Their Impacts on the Performance of a DTN Publish/Subscribe System

Mobile devices carried by people are dynamically networked. Understanding the social structures within the human mobility traces captured from the mobile devices help us design efficient message dissemination schemes. Furthermore, community is an important attribute of future human contact-based networks. People who are in multiple communities are good message carriers. Thus, a distributed community detection scheme that can identify different communities efficiently from the various communication traces e.g. users’ emails, human mobility traces is very useful. In this paper, we first identify nodes that can play key roles from some real-world human mobility and email traces using the traditional social network metrics. Then, we investigate the usefulness of several community extraction schemes that can handle both email and contact traces. Last but not least, we demonstrate how the connector identification helps to improve the performance of a DTN publish/subscribe system.

Case-Based plan diversity

The concept of diversity was successfully introduced for recommender-systems. By displaying results that are not only similar to a target problem but also diverse among themselves, recommender systems have been shown to provide more effective guidance to the user. We believe that similar benefits can be obtained in case-based planning, provided that diversity-enhancement techniques can be adapted appropriately. Our claim is that diversity is truly useful when it refers not only to the initial and goal states of a plan, but also to the sequence of actions the plan consists of. To formalize this characteristic and support our claim, we define the metric of “plan diversity” and put it to test using plans for a real-time strategy game, a domain chosen for the simplicity and clarity of its tasks and the quantifiable results it generates.

Qualitative vs. quantitative plan diversity in case-based planning

Plan diversity has practical value in multiple planning domains, including travel planning, military planning and game planning. Existing methods for obtaining plan diversity fall under two categories: quantitative and qualitative. Quantitative plan diversity is domain-independent and does not require extensive knowledge-engineering effort, but can fail to reflect plan differences that are truly meaningful to users. Qualitative plan diversity is based on domain-specific characteristics which human experts might use to differentiate between plans, thus being able to produce results of greater practical value. However, the previous approach to qualitative plan diversity assumes the availability of a domain metatheory. We propose a case-based planning method for obtaining qualitative plan diversity through the use of distance metrics which incorporate domain-specific content, without requiring a domain metatheory. To our knowledge, this is the first time qualitative plan diversity is being explored in a case-based planning context.

Diverse Plan Generation by Plan Adaptation and by First-Principles Planning: A Comparative Study

Plan diversity has been explored in case-based planning, which relies on the availability of episodic knowledge, and in first-principles planning, which relies on the availability of a complete planning domain model. We present a first comparative study of these two approaches to obtaining diverse plans. We do so by developing a conceptual framework for plan diversity which subsumes both case-based and first-principles diverse plan generation, and using it to contrast two such systems, identifying their relative strengths and weaknesses. To corroborate our analysis, we perform a comparative experimental evaluation of these systems on a real-time strategy game domain.

Goal Reasoning and Trusted Autonomy

This chapter discusses the topic of Goal Reasoning and its relation to Trusted Autonomy. Goal Reasoning studies how autonomous agents can extend their reasoning capabilities beyond their plans and actions, to consider their goals. Such capability allows a Goal Reasoning system to more intelligently react to unexpected events or changes in the environment. We present two models of Goal Reasoning: Goal-Driven Autonomy (GDA) and goal refinement. We then discuss several research topics related to each, and how they relate to the topic of Trusted Autonomy. Finally, we discuss several directions of ongoing work that are particularly interesting in the context of the chapter: using a model of inverse trust as a basis for adaptive autonomy, and studying how Goal Reasoning agents may choose to rebel (i.e., act contrary to a given command).