David V. Pynadath

Towards adjustable autonomy for the real world

Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal two key shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects on actions) to an agents team due to such transfers-of-control. To remedy these problems, this article presents a novel approach to adjustable autonomy, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from an agent to a user or vice versa) and (ii) actions to change an agents pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high-quality individual decisions to be made with minimal disruption to the coordination of the team. We present a mathematical model of transfer-of-control strategies. The model guides and informs the operationalization of the strategies using Markov Decision Processes, which select an optimal strategy, given an uncertain environment and costs to the individuals and teams. The approach has been carefully evaluated, including via its use in a real-world, deployed multi-agent system that assists a research group in its daily activities.

Multiagent teamwork: analyzing the optimality and complexity of key theories and models

Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Thus, we cannot determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP) model, which is general enough to subsume many existing models of multiagent systems. We analyze use the COM-MTDP model to provide a breakdown of the computational complexity of constructing optimal teams under problem domains divided along the dimensions of observability and communication cost. We then exploit the COM-MTDPs ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory, including STEAM. We then derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations. We have implemented a reusable, domain-independent software package based COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.

Toward Team-Oriented Programming

The promise of agent-based systems is leading towards the development of autonomous, heterogeneous agents, designed by a variety of research/industrial groups and distributed over a variety of platforms and environments. Teamwork among these heterogeneous agents is critical in realizing the full potential of these systems and scaling up to the demands of large-scale applications. Unfortunately, development of robust, flexible agent teams is currently extremely difficult. This paper focuses on significantly accelerating the process of building such teams using a simplified, abstract framework called team-oriented programming (TOP). In TOP, a programmer specifies an agent organization hierarchy and the team tasks for the organization to perform, abstracting away from the innumerable coordination plans potentially necessary to ensure robust and flexible team operation. Our TEAMCORE system supports TOP through a distributed, domain-independent layer that integrates core teamwork coordination and communication capabilities. We have recently used TOP to integrate a diverse team of heterogeneous distributed agents in performing a complex task. We outline the current state of our TOP implementation and the outstanding issues in developing such a framework.

Building dynamic agent organizations in cyberspace

The Karma-Teamcore framework focuses on rapidly integrating distributed, heterogeneous agents and tasking them via an abstract team-oriented program. The framework provides wrappers that encapsulate general teamwork reasoning and automatically generate the necessary coordination for robust execution of this abstract program. We describe the Karma-Teamcore framework and present an example of its successful application, namely, the simulated evacuation of civilians stranded in a hostile area.

Adjustable autonomy in real-world multi-agent environments

Through {\em adjustable autonomy} (AA), an agent can dynamically vary the degree to which it acts autonomously, allowing it to exploit human abilities to improve its performance, but without becoming overly dependent and intrusive in its human interaction. AA research is critical for successful deployment of multi-agent systems in support of important human activities. While most previous AA work has focused on individual agent-human interactions, this paper focuses on {\em teams} of agents operating in real-world human organizations. The need for agent teamwork and coordination in such environments introduces novel AA challenges. First, agents must be more judicious in asking for human intervention, because, although human input can prevent erroneous actions that have high team costs, one agents inaction while waiting for a human response can lead to potential miscoordination with the other agents in the team. Second, despite appropriate local decisions by individual agents, the overall team of agents can potentially make global decisions that are unacceptable to the human team. Third, the diversity in real-world human organizations requires that agents gradually learn individualized models of the human members, while still making reasonable decisions even before sufficient data are available. We address these challenges using a multi-agent AA framework based on an adaptive model of users (and teams) that reasons about the uncertainty, costs, and constraints of decisions at {\em all} levels of the team hierarchy, from the individual users to the overall human organization. We have implemented this framework through Markov decision processes, which are well suited to reason about the costs and uncertainty of individual and team actions. Our approach to AA has proven essential to the success of our deployed multi-agent Electric Elves system that assists our research group in rescheduling meetings, choosing presenters, tracking peoples locations, and ordering meals.

Electric Elves: Agent Technology for Supporting Human Organizations

The operation of a human organization requires dozens of everyday tasks to ensure coherence in organizational activities, to monitor the status of such activities, to gather information relevant to the organization, to keep everyone in the organization informed, etc. Teams of software agents can aid humans in accomplishing these tasks, facilitating the organization’s coherent functioning and rapid response to crises, while reducing the burden on humans. Based on this vision, this paper reports on Electric Elves, a system that has been operational, 24/7, at our research institute since June 1, 2000. Tied to individual user workstations, fax machines, voice, mobile devices such as cell phones and palm pilots, Electric Elves has assisted us in routine tasks, such as rescheduling meetings, selecting presenters for research meetings, tracking people’s locations, organizing lunch meetings, etc. We discuss the underlying AI technologies that led to the success of Electric Elves, including technologies devoted to agenthuman interactions, agent coordination, accessing multiple heterogeneous information sources, dynamic assignment of organizational tasks, and deriving information about organization members. We also report the results of deploying Electric Elves in our own research organization.

Adaptive agent integration architectures for heterogeneous team members

With the proliferation of software agents and smart hardware devices there is a growing realization that large-scale problems can be addressed by integration of such standalone systems. This has led to an increasing interest in integration architectures that enable a heterogeneous variety of agents and humans to work together. These agents and humans differ in their capabilities, preferences, the level of autonomy they are willing to grant the integration architecture and their information requirements and performance. The challenge in coordinating such a diverse agent set is that potentially a large number of domain-specific and agent specific coordination plans may be required. We present a novel two-tiered approach to address this coordination problem. We first provide the integration architecture with general purpose teamwork coordination capabilities, but then enable adaptation of such capabilities for the needs or requirements of specific individuals. A key novel aspect of this adaptation is that it takes place in the context of other heterogeneous team members. We are realizing this approach in an implemented distributed agent integration architecture called Teamcore. Experimental results from two different domains are presented.

Adjustable Autonomy for the Real World

Adjustable autonomy refers to agents’ dynamically varying their own autonomy, transferring decision making control to other entities (typically human users) in key situations. Determining whether and when such transfers of control must occur is arguably the fundamental research question in adjustable autonomy. Previous work, often focused on individual agent-human interactions, has provided several different techniques to address this question. Unfortunately, domains requiring collaboration between teams of agents and humans reveals two key shortcomings of these previous techniques. First, these techniques use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects of actions) to an agent’ team due to such transfers of control.

Modeling appraisal in theory of mind reasoning

Cognitive appraisal theories, which link human emotional experience to their interpretations of events happening in the environment, are leading approaches to model emotions. In this paper, we investigate the computational modeling of appraisal in a multi-agent decision-theoretic framework using POMDP based agents. We illustrate how five key appraisal dimensions (motivational relevance, motivation congruence, accountability, control and novelty) can be derived from the processes and information required for the agents decision-making and belief maintenance. Through this illustration, we not only provide a solution for computationally modeling emotion in POMDP based agents, but also demonstrate the tight relationship between emotion and cognition. Our model of appraisal is applied to three different scenarios to illustrate its usage. We also discuss how the modeling of theory of mind (recursive beliefs about self and others) is critical for simulating social emotions.

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.