This paper explores how an autonomous agent can model dynamic environments and use that knowledge to improve its behavior. This capability is of particular importance for persistent agents, or long-term autonomy. Inspiration is drawn from circadian rhythms in nature, which drive periodic behavior in many organisms. In our approach, the chemical oscillators from nature are replaced with methods from time series analysis designed for forecasting complex season patterns. This model is incorporated into a behavior-based architecture as an advanced-percept, providing future estimates of the environment rather than current measurements. A simulated application of a janitor robot working in an environment with heavy pedestrian traffic was created as a testbed. Experimental data used real world pedestrian traffic counts and showed an agent using online forecasting of future traffic outperformed both a reactive, sensor-based, strategy and a strategy with a deterministic schedule.
Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We give a historical overview of research leading to the current state of the art in DCP and describe research in distributed and continual planning.
Abstraction and aggregation are useful for increasing speed of inference in and easing knowledge acquisition of belief networks. This paper presents previous research on belief network abstraction and aggregation, discusses its hmitations, and outlines directions for future research. Introduction Abstraction and aggregation have been used in several areas in artificial intelligence, including in planning, model-based reasoning, and reasoning under uncertainty. For reasoning under uncertainty, the framework of decision theory and in particular the notion of influence diagram (or decision diagram) has proven fruitful. An influence diagram is essentially a graph, where nodes are chance nodes, decision (or action) nodes, utility (or Value) nodes.
Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations. While extending this paradigm to autonomous agents is a well-studied problem in general, there are two particular aspects that have largely been overlooked: (1) that the learning is done from observation only (i.e., without explicit action information), and (2) that the learning is typically done very quickly. In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. First, we allow the agent to acquire experience in a self-supervised fashion. This experience is used to develop a model which is then utilized to learn a particular task by observing an expert perform that task without the knowledge of the specific actions taken. We experimentally compare BCO to imitation learning methods, including the state-of-the-art, generative adversarial imitation learning (GAIL) technique, and we show comparable task performance in several different simulation domains while exhibiting increased learning speed after expert trajectories become available.