Collaborating Authors

Online Active Perception for Partially Observable Markov Decision Processes with Limited Budget Artificial Intelligence

-- Active perception strategies enable an agent to selectively gather information in a way to improve its performance. In applications in which the agent does not have prior knowledge about the available information sources, it is crucial to synthesize active perception strategies at runtime. We consider a setting in which at runtime an agent is capable of gathering information under a limited budget. We pose the problem in the context of partially observable Markov decision processes. We propose a generalized greedy strategy that selects a subset of information sources with near-optimality guarantees on uncertainty reduction. Our theoretical analysis establishes that the proposed active perception strategy achieves near-optimal performance in terms of expected cumulative reward. We demonstrate the resulting strategies in simulations on a robotic navigation problem. An intelligent system should be able to exploit the available information in its surroundings toward better accomplishment of its task.

Exploiting Submodular Value Functions For Scaling Up Active Perception Artificial Intelligence

In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. While partially observable Markov decision processes (POMDPs) provide a natural model for such problems, reward functions that directly penalize uncertainty in the agent's belief can remove the piecewise-linear and convex property of the value function required by most POMDP planners. Furthermore, as the number of sensors available to the agent grows, the computational cost of POMDP planning grows exponentially with it, making POMDP planning infeasible with traditional methods. In this article, we address a twofold challenge of modeling and planning for active perception tasks. We show the mathematical equivalence of $\rho$POMDP and POMDP-IR, two frameworks for modeling active perception tasks, that restore the PWLC property of the value function. To efficiently plan for active perception tasks, we identify and exploit the independence properties of POMDP-IR to reduce the computational cost of solving POMDP-IR (and $\rho$POMDP). We propose greedy point-based value iteration (PBVI), a new POMDP planning method that uses greedy maximization to greatly improve scalability in the action space of an active perception POMDP. Furthermore, we show that, under certain conditions, including submodularity, the value function computed using greedy PBVI is guaranteed to have bounded error with respect to the optimal value function. We establish the conditions under which the value function of an active perception POMDP is guaranteed to be submodular. Finally, we present a detailed empirical analysis on a dataset collected from a multi-camera tracking system employed in a shopping mall. Our method achieves similar performance to existing methods but at a fraction of the computational cost leading to better scalability for solving active perception tasks.

A POMDP Extension with Belief-dependent Rewards

Neural Information Processing Systems

Partially Observable Markov Decision Processes (POMDPs) model sequential decision-making problems under uncertainty and partial observability. Unfortunately, some problems cannot be modeled with state-dependent reward functions, e.g., problems whose objective explicitly implies reducing the uncertainty on the state. To that end, we introduce rho-POMDPs, an extension of POMDPs where the reward function rho depends on the belief state. We show that, under the common assumption that rho is convex, the value function is also convex, what makes it possible to (1) approximate rho arbitrarily well with a piecewise linear and convex (PWLC) function, and (2) use state-of-the-art exact or approximate solving algorithms with limited changes.

Efficient Decision-Theoretic Target Localization

AAAI Conferences

Partially observable Markov decision processes (POMDPs) offer a principled approach to control under uncertainty. However, POMDP solvers generally require rewards to depend only on the state and action. This limitation is unsuitable for information-gathering problems, where rewards are more naturally expressed as functions of belief. In this work, we consider target localization, an information-gathering task where an agent takes actions leading to informative observations and a concentrated belief over possible target locations. By leveraging recent theoretical and algorithmic advances, we investigate offline and online solvers that incorporate belief-dependent rewards. We extend SARSOP--a state-of-the-art offline solver--to handle belief-dependent rewards, exploring different reward strategies and showing how they can be compactly represented. We present an improved lower bound that greatly speeds convergence. POMDP-lite, an online solver, is also evaluated in the context of information-gathering tasks. These solvers are applied to control a hex-copter UA V searching for a radio frequency source--a challenging real-world problem.

Policy Iteration for Decentralized Control of Markov Decision Processes Artificial Intelligence

Coordination of distributed agents is required for problems arising in many areas, including multi-robot systems, networking and e-commerce. As a formal framework for such problems, we use the decentralized partially observable Markov decision process (DEC-POMDP). Though much work has been done on optimal dynamic programming algorithms for the single-agent version of the problem, optimal algorithms for the multiagent case have been elusive. The main contribution of this paper is an optimal policy iteration algorithm for solving DEC-POMDPs. The algorithm uses stochastic finite-state controllers to represent policies. The solution can include a correlation device, which allows agents to correlate their actions without communicating. This approach alternates between expanding the controller and performing value-preserving transformations, which modify the controller without sacrificing value. We present two efficient value-preserving transformations: one can reduce the size of the controller and the other can improve its value while keeping the size fixed. Empirical results demonstrate the usefulness of value-preserving transformations in increasing value while keeping controller size to a minimum. To broaden the applicability of the approach, we also present a heuristic version of the policy iteration algorithm, which sacrifices convergence to optimality. This algorithm further reduces the size of the controllers at each step by assuming that probability distributions over the other agents actions are known. While this assumption may not hold in general, it helps produce higher quality solutions in our test problems.