Using the relaxed plan heuristic to select goals in oversubscription planning problems

Oversubscription planning (OSP) appears in many real problems where nding a plan achieving all goals is infeasi- ble. The objective is to nd a feasible plan reaching a goal sub- set while maximizing some measure of utility. In this paper, we present a new technique to select goals \a priori" for problems in which a cost bound prevents all the goals from being achieved. It uses estimations of distances between goals, which are com- puted using relaxed plans. Using these distances, a search in the space of subsets of goals is performed, yielding a new set of goals to plan for. A revised planning problem can be created and solved, taking into account only the selected goals. We present experiments in six di erent domains with good results.This work has been partially supported by MICIIN TIN2008-06701-C03-03 and CCG10-UC3M/TIC-5597 projects.Publicad

From perception to action and vice versa: a new architecture showing how perception and action can modulate each other simultaneously

Presentado en: 6th European Conference on Mobile Robots (ECMR) Sep 25-27, 2013 Barcelona, SpainArtificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. From the Automated Planning point of view, a relevant area can be seen as an area where the objects involved in the execution of a plan are located. Thus, the planning system should guide the attention model to track relevant objects. But, at the same time, the perceived objects may constrain or provide new information that could suggest the modification of a current plan. Therefore, a plan that is being executed should be adapted or recomputed taking into account actual information perceived from the world. In this work, we introduce an architecture that creates a symbiosis between the planning and the attention modules of a robotic system, linking visual features with high level behaviours. The architecture is based on the interaction of an oversubscription planner, that produces plans constrained by the information perceived from the vision system, and an object-based attention system, able to focus on the relevant objects of the plan being executed.Spanish MINECO projects TIN2008-06196, TIN2012-38079-C03-03 and TIN2012-38079-C03-02. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Planning graph heuristics for selecting objectives in over-subscription planning problems

Partial Satisfaction or Over-subscription Planning problems arise in many real world applications. Applications in which the planning agent does not have enough resources to accomplish all of their given goals, requiring plans that satisfy only a subset of them. Solving such partial satisfaction planning (PSP) problems poses several challenges, from new models for handling plan quality to efficient heuristics for selecting the most beneficial goals. In this paper, we extend planning graph-based reachability heuristics with mutex analysis to overcome complex goal interactions in PSP problems. We start by describing one of the most general PSP problems, the PSP NET BENEFIT problem, where actions have execution costs and goals have utilities. Then, we present AltWlt, 1 our heuristic approach augmented with a multiple goal set selection process and mutex analysis. Our empirical studies show that AltWlt is able to generate the most beneficial solutions, while incurring only a small fraction of the cost of other PSP approaches

From perception to action and vice versa: A new architecture showing how perception and action can modulate each other simultaneously

The proceeding at: 6th European Conference on Mobile Robots.Took place in September 25-27, 2013, in Barcelona, Spain.Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. From the Automated Planning point of view, a relevant area can be seen as an area where the objects involved in the execution of a plan are located. Thus, the planning system should guide the attention model to track relevant objects. But, at the same time, the perceived objects may constrain or provide new information that could suggest the modification of a current plan. Therefore, a plan that is being executed should be adapted or recomputed taking into account actual information perceived from the world. In this work, we introduce an architecture that creates a symbiosis between the planning and the attention modules of a robotic system, linking visual features with high level behaviours. The architecture is based on the interaction of an oversubscription planner, that produces plans constrained by the information perceived from the vision system, and an object-based attention system, able to focus on the relevant objects of the plan being executed.This work has been partially granted by the Spanish Ministerio de Economía y Competitividad (MINECO) projects no. TIN2008-06196, TIN2012-38079-C03-03 and TIN2012-38079-C03-02. It has been also granted by Universidad de Málaga, International Campus of Excellence Andalucía Tech.Publicad

Planning with Continuous Resources in Stochastic Domains

We consider the problem of optimal planning in stochastic domains with metric resource constraints. Our goal is to generate a policy whose expected sum of rewards is maximized for a given initial state. We consider a general formulation motivated by our application domain--planetary exploration--in which the choice of an action at each step may depend on the current resource levels. We adapt the forward search algorithm AO* to handle our continuous state space efficiently

GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency

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