Person:

Amir, Ofra

This paper envisions a multi-agent system that assists patients and their health care providers. This system would support a diverse, evolving team in formulating, monitoring and revising a shared "care plan" that operates on multiple time scales in uncertain environments. It would also enhance communication of health information within this planning framework. The coordination of care for children with complex conditions (CCC), which is a compelling societal need, is presented as a model environment in which to develop and assess such systems. The design of algorithms and techniques needed to realize this vision would yield agents capable of being collaborative partners in health care delivery broadly as well as in other environments with similar properties such as rescue and rebuilding after natural disasters. This paper describes the key characteristics of collaborative health care plan support, defines a set of essential capabilities for autonomous "care-augmenting software agents", and discusses three major multi-agents systems research challenges that building such agents raises: evolving long-term plan management, enhancing team interactions, and leveraging human computation for care plan customization.

Teamwork and care coordination are of increasing importance to health care delivery and patient safety and health. This paper describes our initial work on developing agents that are able to make intelligent information sharing decisions to support a diverse, evolving team of care providers in constructing and maintaining a shared plan that operates in uncertain environments and over a long time horizon.

This paper proposes a mapping between multi-agent pathfinding (MAPF) and combinatorial auctions (CAs). In MAPF, agents need to reach their goal destinations without colliding. Algorithms for solving MAPF aim at assigning agents non-conflicting paths that minimize agents’ travel costs. In CA problems, agents bid over bundles of items they desire. Auction mechanisms aim at finding an allocation of bundles that maximizes social welfare. In the proposed mapping of MAPF to CAs, agents bid on paths to their goals and the auction allocates non-colliding paths to the agents. Using this formulation, auction mechanisms can be naturally used to solve a range of MAPF problem variants. In particular, auction mechanisms can be applied to non-cooperative settings with self-interested agents while providing optimality guarantees and robustness to manipulations by agents. The paper further shows how to efficiently implement an auction mechanism for MAPF, utilizing methods and representations from both the MAPF and CA literatures.

Children with complex health conditions require care from a large, diverse team of caregivers that includes multiple types of medical professionals, parents and community support organizations. Coordination of their outpatient care, essential for good outcomes, presents major challenges. Extensive healthcare research has shown that the use of integrated, team-based care plans improves care coordination, but such plans are rarely deployed in practice. This paper reports on a study of care teams treating children with complex conditions at a major university tertiary care center. This study investigated barriers to plan implementation and resultant care coordination problems. It revealed the complex nature of teamwork in complex care, which poses challenges to team coordination that extend beyond those identified in prior work and handled by existing coordination systems. The paper builds on a computational teamwork theory to identify opportunities for technology to support increased plan-based complex-care coordination and to propose design approaches for systems that enable and enhance such coordination.

This paper defines the "Single Agent in a Team Decision" (SATD) problem. SATD differs from prior multi-agent communication problems in the assumptions it makes about teammates' knowledge of each other's plans and possible observations. The paper proposes a novel integrated logical-decision-theoretic approach to solving SATD problems, called MDP-PRT. Evaluation of MDP-PRT shows that it outperforms a previously proposed communication mechanism that did not consider the timing of communication and compares favorably with a coordinated Dec-POMDP solution that uses knowledge about all possible observations.

Many documents (e.g., academic papers, government reports) are typically written by multiple authors. While existing tools facilitate and support such collaborative efforts (e.g., Dropbox, Google Docs), these tools lack intelligent information sharing mechanisms. Capabilities such as “track changes” and “diff” visualize changes to authors, but do not distinguish between minor and major edits and do not consider the possible effects of edits on other parts of the document. Drawing collaborators’ attention to specific edits and describing them remains the responsibility of authors. This paper presents our initial work toward the development of a collaborative system that supports multi-author writing. We describe methods for tracking paragraphs, identifying significant edits, and predicting parts of the paper that are likely to require changes as a result of previous edits. Preliminary evaluation of these methods shows promising results.

Children with complex health conditions require care from a large, diverse set of caregivers that includes parents and community support organizations as well as multiple types of medical professionals. Coordination of their care is essential for good outcomes, and extensive research has shown that the use of integrated, team -based care plans improves care coordination. Care plans, however, are rarely deployed in practice. This paper describes barriers to effective implementation of care plans in complex care revealed by a study of care providers treating such children. It draws on teamwork theories, identifying ways AI capabilities could enhance care plan use; describes the design of GoalKeeper, a system to support providers use of care plans; and describes initial work toward information sharing algorithms for such systems.

Agents learning how to act in new environments can benefit from input from more experienced agents or humans. This paper studies interactive teaching strategies for identifying when a student can benefit from teacher-advice in a reinforcement learning framework. In student-teacher learning, a teacher agent can advise the student on which action to take. Prior work has considered heuristics for the teacher to choose advising opportunities. While these approaches effectively accelerate agent training, they assume that the teacher constantly monitors the student. This assumption may not be satisfied with human teachers, as people incur cognitive costs of monitoring and might not always pay attention. We propose strategies for a teacher and a student to jointly identify advising opportunities so that the teacher is not required to constantly monitor the student. Experimental results show that these approaches reduce the amount of attention required from the teacher compared to teacher-initiated strategies, while maintaining similar learning gains. The empirical evaluation also investigates the effect of the information communicated to the teacher and the quality of the student’s initial policy on teaching outcomes

Complex collaborative activities such as treating patients, co-authoring documents and developing software are often characterized by teamwork that is loosely coupled and extends in time. To remain coordinated and avoid conflicts, team members need to identify dependencies between their activities — which though loosely coupled may interact — and share information appropriately. The loose-coupling of tasks increases the difficulty of identifying dependencies, with the result that team members often lack important information or are overwhelmed by irrelevant information. This paper formalizes a new multi-agent systems problem, Information Sharing in Loosely-Coupled Extended-Duration Teamwork (ISLET). It defines a new representation, Mutual Influence Potential Networks (MIP-Nets) and an algorithm, MIP-DOI, that uses this representation to determine the information that is most relevant to each team member. Importantly, because the extended duration of the teamwork precludes team members from developing complete plans in advance, the MIP-Nets approach, unlike prior work on information sharing, does not rely on a priori knowledge of a team’s possible plans. Instead, it models collaboration patterns and dependencies among people and their activities based on team-member interactions. Empirical evaluations show that this approach is able to learn collaboration patterns and identify relevant information to share with team members.