Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici

A Temporal Logic for Modelling Activities of Daily Living

Behaviour support technology is aimed at assisting people in organizing their Activities of Daily Living (ADLs). Numerous frameworks have been developed for activity recognition and for generating specific types of support actions, such as reminders. The main goal of our research is to develop a generic formal framework for representing and reasoning about ADLs and their temporal relations. This framework should facilitate modelling and reasoning about 1) durative activities, 2) relations between higher-level activities and subactivities, 3) activity instances, and 4) activity duration. In this paper we present a temporal logic as an extension of the logic TPTL for specification of real-time systems. Our logic TPTL_{bih} is defined over Behaviour Identification Hierarchies (BIHs) for representing ADL structure and typical activity duration. To model execution of ADLs, states of the temporal traces in TPTL_{bih} comprise information about the start, stop and current execution of activities. We provide a number of constraints on these traces that we stipulate are desired for the accurate representation of ADL execution, and investigate corresponding validities in the logic. To evaluate the expressivity of the logic, we give a formal definition for the notion of Coherence for (complex) activities, by which we mean that an activity is done without interruption and in a timely fashion. We show that the definition is satisfiable in our framework. In this way the logic forms the basis for a generic monitoring and reasoning framework for ADLs

The significance of bidding, accepting and opponent modeling in automated negotiation

Given the growing interest in automated negotiation, the search for effective strategies has produced a variety of different negotiation agents. Despite their diversity, there is a common structure to their design. A negotiation agent comprises three key components: the bidding strategy, the opponent model and the acceptance criteria. We show that this three-component view of a negotiating architecture not only provides a useful basis for developing such agents but also provides a useful analytical tool. By combining these components in varying ways, we are able to demonstrate the contribution of each component to the overall negotiation result, and thus determine the key contributing components. Moreover, we are able to study the interaction between components and present detailed interaction effects. Furthermore, we find that the bidding strategy in particular is of critical importance to the negotiator's success and far exceeds the importance of opponent preference modeling techniques. Our results contribute to the shaping of a research agenda for negotiating agent design by providing guidelines on how agent developers can spend their time most effectively

The first automated negotiating agents competition (ANAC 2010)

Motivated by the challenges of bilateral negotiations between people and automated agents we organized the first automated negotiating agents competition (ANAC 2010). The purpose of the competition is to facilitate the research in the area bilateral multi-issue closed negotiation. The competition was based on the Genius environment, which is a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. The first competition was held in conjunction with the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-10) and was comprised of seven teams. This paper presents an overview of the competition, as well as general and contrasting approaches towards negotiation strategies that were adopted by the participants of the competition. Based on analysis in post--tournament experiments, the paper also attempts to provide some insights with regard to effective approaches towards the design of negotiation strategies