Hyuckchul Jung

A Dynamic Distributed Constraint Satisfaction Approach to Resource Allocation

In distributed resource allocation a set of agents must assign their resources to a set of tasks. This problem arises in many real-world domains such as disaster rescue, hospital scheduling and the domain described in this paper: distributed sensor networks. Despite the variety of approaches proposed for distributed resource allocation, a systematic formalization of the problem and a general solution strategy are missing. This paper takes a step towards this goal by proposing a formalization of distributed resource allocation that represents both dynamic and distributed aspects of the problem and a general solution strategy that uses distributed constraint satisfaction techniques. This paper defines the notion of Dynamic Distributed Constraint Satisfaction Problem (DyDCSP) and proposes two generalized mappings from distributed resource allocation to DyDCSP, each proven to correctly perform resource allocation problems of specific difficulty and this theoretical result is verified in practice by an implementation on a real-world distributed sensor network.

Argumentation as distributed constraint satisfaction: applications and results

Conflict resolution is a critical problem in distributed and collaborative multi-agent systems. Negotiation via argumentation (NVA), where agents provide explicit arguments or justifications for their proposals for resolving conflicts, is an effective approach to resolve conflicts. Indeed, we are applying argumentation in some real-world multi-agent applications. However, a key problem in such applications is that a well-understood computational model of argumentation is currently missing, making it difficult to investigate convergence and scalability of argumentation techniques, and to understand and characterize different collaborative NVA strategies in a principled manner. To alleviate these difficulties, we present distributed constraint satisfaction problem (DCSP) as a computational model for investigating NVA. We model argumentation as constraint propagation in DCSP. This model enables us to study convergence properties of argumentation, and formulate and experimentally compare 16 different NVA strategies with different levels of agent cooperativeness towards others. One surprising result from our experiments is that maximizing cooperativeness is not necessarily the best strategy even in a completely cooperative environment. The paper illustrates the usefulness of these results in applying NVA to multi-agent systems, as well as to DCSP systems in general.

New Developments in Ontology-Based Policy Management: Increasing the Practicality and Comprehensiveness of KAoS

The KAoS policy management framework pioneered the use of semantically-rich ontological representation and reasoning to specify, analyze, deconflict, and enforce policies [9, 10]. The framework has continued to evolve over the last five years, inspired by both technological advances and the practical needs of its varied applications. In this paper, we describe how these applications have motivated the partitioning of components into a well-defined three-layer policy management architecture that hides ontology complexity from the human user and from the policy-governed system. The power of semantic reasoning is embedded in the middle layer of the architecture where it can provide the most benefit. We also describe how the policy semantics of the core KAoS policy ontology has grown in its comprehensiveness. The flexible and mature architecture of KAoS enables straightforward integration with a variety of deployment platforms, ranging from highly distributed systems, such as the AFRL information management system, to human-robotic interaction, to dynamic management of quality-of-service and cross-domain information management of wireless networks in resource-constrained or security-sensitive environments.

Dynamic Distributed Resource Allocation: A Distributed Constraint Satisfaction Approach

In distributed resource allocation a set of agents must assign their resources to a set of tasks. This problem arises in many real-world domains such as distributed sensor networks, disaster rescue, hospital scheduling and others. Despite the variety of approaches proposed for distributed resource allocation, a systematic formalization of the problem, explaining the different sources of difficulties, and a formal explanation ofthe strengths and limitations ofk ey approaches is missing. We take a step towards this goal by proposing a formalization of distributed resource allocation that represents both dynamic and distributed aspects ofthe problem. We define four categories ofdif ficulties ofthe problem. To address this formalized problem, the paper defines the notion of Dynamic Distributed Constraint Satisfaction Problem (DyDCSP). The central contribution of the paper is a generalized mapping from distributed resource allocation to DyDCSP. This mapping is proven to correctly perform resource allocation problems of specific difficulty. This theoretical result is verified in practice by an implementation on a real-world distributed sensor network.

Dimensions of adjustable autonomy and mixed-initiative interaction

Several research groups have grappled with the problem of characterizing and developing practical approaches for implementing adjustable autonomy and mixed-initiative interaction in deployed systems. However, each group takes a little different approach and uses variations of the same terminology in a somewhat different fashion. In this chapter, we will describe some common dimensions in an effort to better understand these important but ill-characterized topics. We are developing a formalism and implementation of these concepts as part of the KAoS framework in the context of our research on policy-governed autonomous systems.

Conflicts in teamwork: hybrids to the rescue

Today within the AAMAS community, we see at least four competing approaches to building multiagent systems: belief-desire-intention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, and auctions or game-theoretic approaches. While there is exciting progress within each approach, there is a lack of cross-cutting research. This paper highlights hybrid approaches for multiagent teamwork. In particular, for the past decade, the TEAMCORE research group has focused on building agent teams in complex, dynamic domains. While our early work was inspired by BDI, we will present an overview of recent research that uses DCOPs and distributed POMDPs in building agent teams. While DCOP and distributed POMDP algorithms provide promising results, hybrid approaches help us address problems of scalability and expressiveness. For example, in the BDI-POMDP hybrid approach, BDI team plans are exploited to improve POMDP tractability, and POMDPs improve BDI team plan performance. We present some recent results from applying this approach in a Disaster Rescue simulation domain being developed with help from the Los Angeles Fire Department.

From Tools to Teammates: Joint Activity in Human-Agent-Robot Teams

Coordination is an essential ingredient of joint activity in human-agent-robot teams. In this paper, we discuss some of the challenges and requirements for successful coordination, and briefly how we have used KAoS HART services framework to support coordination in a multi-team human-robot field exercise.

Performance models for large scale multiagent systems: using distributed POMDP building blocks

Given a large group of cooperative agents, selecting the right coordination or conflict resolution strategy can have a significant impact on their performance (e.g., speed of convergence). While performance models of such coordination or conflict resolution strategies could aid in selecting the right strategy for a given domain, such models remain largely uninvestigated in the multiagent literature. This paper takes a step towards applying the recently emerging distributed POMDP (partially observable Markov decision process) frameworks, such as MTDP (Markov team decision process), in service of creating such performance models. To address issues of scale-up, we use small-scale models, called building blocks that represent the local interaction among a small group of agents. We discuss several ways to combine building blocks for performance prediction of a larger-scale multiagent system.We present our approach in the context of DCSPs (distributed constraint satisfaction problems), where we first show that there is a large bank of conflict resolution strategies and no strategy dominates all others across different domains. By modeling and combining building blocks, we are able to predict the performance of five different DCSP strategies for four different domain settings, for a large-scale multiagent system. Our approach thus points the way to new tools for strategy analysis and performance modeling in multiagent systems in general.

Coordination in Human-Agent-Robot Teamwork

Coordination is an essential ingredient of a teamwork- centered approach to autonomy. In this paper, we discuss some of the challenges and requirements for successful coordination, and briefly how we have used KAoS HART services framework to support coordination in a multi- team human-robot field exercise.

Policy-based coordination in joint human-agent activity

In this paper, we outline an approach to policy-based coordination in joint human-agent activity. The approach is grounded in a theory of joint activity originally developed in the context of discourse, and now applied to the broader realm of human-agent interaction. We have been gradually implementing selected aspects of policy-based coordination within the KAoS services framework and have been developing a body of examples that guide additional testing of these ideas through detailed studies of work practice.