6 accepted papers
Stochastic planning problems are typically modeled as Markov Decision Processes, in which actions are assumed to be instantaneous and applied sequentially. Yet, real-world actions often have durations and are applied concurrently. This paper presents an online planning approach that can deal with du
We describe AOS, the first general-purpose system for model-based control of autonomous robots using AI planning that fully supports partial observability and noisy sensing. The AOS provides a code-based language for specifying a generative model of the system, making model specification easier and
Actions description languages (ADLs), such as STRIPS, PDDL, and RDDL specify the input format for planning algorithms. Unfortunately, their syntax is familiar to planning experts only, and not to potential users of planning technology. Moreover, this syntax limits the ability to describe complex and…
Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple singl…
Regular Decision Processes (RDPs) are a recently introduced model that extends MDPs with non-Markovian dynamics and rewards. The non-Markovian behavior is restricted to depend on regular properties of the history. These can be specified using regular expressions or formulas in linear dynamic logic o…
Despite the existence of powerful formal languages for writing robot controllers, most existing functional modules are written using standard programming languages. The existence of such a code base raises critical challenges: 1. How to enable automated analysis, monitoring, and reuse of existing co…