Demonstration of Teamwork in Uncertain Domains using Hybrid BDI-POMDP systems


Tapana Gupta, Pradeep Varakantham, Timothy W. Rauenbusch, and Milind Tambe. 2007. “Demonstration of Teamwork in Uncertain Domains using Hybrid BDI-POMDP systems .” In Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) Demo Track.


Personal Assistant agents are becoming increasingly important in a variety of application domains in offices, at home, for medical care and many others [5, 1]. These agents are required to constantly monitor their environment (including the state of their users), and make periodic decisions based on their monitoring. For example, in an office environment, agents may need to monitor the location of their user in order to ascertain whether the user would be able to make it on time to a meeting [5]. Or, they may be required to monitor the progress of a user on a particular assignment and decide whether or not the user would be able to meet the deadline for completing the assignment. Teamwork between such agents is important in Personal Assistant applications to enable agents working together to achieve a common goal (such as finishing a project on time). This working demonstration shows a hybrid(BDI-POMDP) approach to accomplish such teamwork. Agents must be able to make decisions despite observational uncertainty in the environment. For example, if the user is busy and does not respond to a request from its personal assistant agent, the agent loses track of the user’s progress and hence, cannot determine it with certainty. Also, an incorrect action on the agent’s part can have undesirable consequences. For example, an agent might reallocate a task again and again even if there is sufficient progress on the task. In the past, teamwork among Personal Assistant agents typically has not addressed such observational uncertainty. Markov Decision Processes [5] have been used to model the agent’s environment, with simplifying assumptions regarding either observational uncertainty in the environment or the agent’s observational abilities.

Partially Observable Markov Decision Processes (POMDPs) are equipped to deal with the inherent uncertainty in Personal Assistant domains. Computational complexity has been a major hurdle in deploying POMDPs in real-world application domains, but the emergence of new exact and approximate techniques [8] recently shows much promise in being able to compute a POMDP policy for an agent in real time. In this demonstration, we actually deploy POMDPs to compute the Adjustable Autonomy policy for an agent based on which the agent makes decisions. Integrating such POMDPs with architectures that enble teamwork among personal assistants is then the next key part of our demonstration. Several teamwork models have been developed over the past few years to handle communication and coordination between agents [7]. Machinetta [6] is a proxy-based integration architecture for coordinating teams of heterogeneous entities (e.g. robots, agents, persons), which builds on the STEAM teamwork model. Machinetta is designed to meet key challenges such as effective utilization of diverse capabilities of group members, improving coordination between agents by overcoming challenges posed by the environment and reacting to changes in the environment in a flexible manner. We use Machinetta proxies to co-ordinate the agents in our demonstration. Machinetta enables integrating POMDPs and also enables interfacing with BDI architectures that may provide us team plans. In particular, we interface with the SPARK agent framework [2] being developed at the Artificial Intelligence Center of SRI international. SPARK is a Belief-Desire-Intention (BDI) style agent framework grounded in a model of procedural reasoning. This architecture allows the development of active systems that interact with a constantly changing and unpredictable world. By using BDI-based approaches for generating team plans for agents as well as communication and coordination, and POMDPs for adjustable autonomy decision making, we arrive at a hybrid model for multiagent teamwork [3] in Personal Assistant applications. The following sections describe the application domain in which we deploy this hybrid system as well as the interaction between various components of the system, and its working.

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