4 research outputs found
Finding Centroids and Minimum Covering States in Planning
In automated planning, the most common task consists of finding a plan that achieves a set of goals. In this paper, we focus on a different task; that of finding states that minimize some goal-related metric. First, we present some domains for which that task is useful. Second, we propose two of such types of states: (1) centroid states, which minimize the distance to all the goals in the problem; and (2) minimum covering states, which minimize the maximum distance to any of the goals. Third, we define optimal and suboptimal algorithms to find such states. Finally, we show some experimental results in planning instances from different domains
Goal reasoning for autonomous agents using automated planning
Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely
a plan, which achieves a goal from a given initial state. Most planning research
consider goals are provided by a external user, and agents just have to find a
plan to achieve them. However, there exist many real world domains where
agents should not only reason about their actions but also about their goals,
generating new ones or changing them according to the perceived environment.
In this thesis we aim at broadening the goal reasoning capabilities of planningbased
agents, both when acting in isolation and when operating in the same
environment as other agents.
In single-agent settings, we firstly explore a special type of planning tasks
where we aim at discovering states that fulfill certain cost-based requirements
with respect to a given set of goals. By computing these states, agents are able
to solve interesting tasks such as find escape plans that move agents in to safe
places, hide their true goal to a potential observer, or anticipate dynamically arriving
goals. We also show how learning the environment’s dynamics may help
agents to solve some of these tasks. Experimental results show that these states
can be quickly found in practice, making agents able to solve new planning
tasks and helping them in solving some existing ones.
In multi-agent settings, we study the automated generation of goals based on
other agents’ behavior. We focus on competitive scenarios, where we are interested
in computing counterplans that prevent opponents from achieving their
goals. We frame these tasks as counterplanning, providing theoretical properties
of the counterplans that solve them. We also show how agents can benefit
from computing some of the states we propose in the single-agent setting to
anticipate their opponent’s movements, thus increasing the odds of blocking
them. Experimental results show how counterplans can be found in different
environments ranging from competitive planning domains to real-time strategy
games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert