We present POMCoP, a system for online planning in collaborative domains that reasons about how its actions will affect its understanding of human intentions, and demonstrate its use in building sidekicks for cooperative games. POMCoP plans in belief space. It explicitly represents its uncertainty about the intentions of its human ally, and plans actions which reveal those intentions or hedge against its uncertainty. This allows POMCoP to reason about the usefulness of incorporating information gathering actions into its plans, such as asking questions, or simply waiting to let humans reveal their intentions. We demonstrate POMCoP by constructing a sidekick for a cooperative pursuit game, and evaluate its effectiveness relative to MDP-based techniques that plan in state space, rather than belief space.
The problem of optimal planning under uncertainty in collaborative multi-agent domains is known to be deeply intractable but still demands a solution. This thesis will explore principled approximation methods that yield tractable approaches to planning for AI assistants, which allow them to understand the intentions of humans and help them achieve their goals. AI assistants are ubiquitous in video games, mak- ing them attractive domains for applying these planning techniques. However, games are also challenging domains, typically having very large state spaces and long planning horizons. The approaches in this thesis will leverage recent advances in Monte-Carlo search, approximation of stochastic dynamics by deterministic dynamics, and hierarchical action representation, to handle domains that are too complex for existing state of the art planners. These planning techniques will be demonstrated across a range of video game domains.
This paper describes a robot controller which uses probabilistic decision-making techniques at the highest-level of behavior control. The POMDP-based robot controller has the ability to incorporate noisy and partial sensor information, and can arbitrate between information gathering and performance-related actions. The complexity of the robot control domain requires a POMDP model that is beyond the capability of current exact POMDP solvers, therefore we present a hierarchical variant of the POMDP model which exploits structure in the problem domain to accelerate planning. This POMDP controller is implemented and tested onboard a mobile robot in the context of an interactive service task. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide guidance and information to elderly residents with mild physical and cognitive disabilities.
The number of applications of service robotics in public spaces such as hospitals, museums and malls is a growing trend. Public spaces, however, provide several challenges to the robot, and specifically with its planning capabilities: they need to cope with a dynamic and uncertain environment and are subject to particular human-robot interaction constraints. A major challenge is the Joint Intention problem. When cooperating with humans, a persistent commitment to achieve a shared goal cannot be always assumed, since they have an unpredictable behavior and may be distracted in environments as dynamic and uncertain as public spaces, and even more so if the human agents are customers,visitors or bystanders. In order to address such issues in a decision-making context, we present a framework based on Hierarchical Factored POMDPs. We describe the general method for ensuring the Joint Intention between human and robot , the hierarchical structure and the Value Decomposition method adopted to build it.We also provide an example application scenario: an Escort Task in a shopping mall for guiding a customer towards a desired point of interest.
Collaboration between agents and players within games is a ripe area for exploration. As with adversarial AI, collaborative agents are challenged to accurately model players and adapt their behavior accordingly. The task of cooperation, however, allows for communication between teammates that can prove beneficial in coordinating joint actions and plans. Furthermore, we propose extending established multi-agent communication paradigms to include transfer of information pertinent to player models. By querying goal and preference information from a player, an agent can reduce uncertainty in coordination domains, allowing for more effective planning. We discuss the challenges as well as the planned development and evaluation of the system.