18 research outputs found
Planning with Incomplete Information
Planning is a natural domain of application for frameworks of reasoning about
actions and change. In this paper we study how one such framework, the Language
E, can form the basis for planning under (possibly) incomplete information. We
define two types of plans: weak and safe plans, and propose a planner, called
the E-Planner, which is often able to extend an initial weak plan into a safe
plan even though the (explicit) information available is incomplete, e.g. for
cases where the initial state is not completely known. The E-Planner is based
upon a reformulation of the Language E in argumentation terms and a natural
proof theory resulting from the reformulation. It uses an extension of this
proof theory by means of abduction for the generation of plans and adopts
argumentation-based techniques for extending weak plans into safe plans. We
provide representative examples illustrating the behaviour of the E-Planner, in
particular for cases where the status of fluents is incompletely known.Comment: Proceedings of the 8th International Workshop on Non-Monotonic
Reasoning, April 9-11, 2000, Breckenridge, Colorad
Minimizing the number of actions in parallel planning
International audienceIn the domain of classical planning one distinguishes plans which are optimal in their number of actions, they are referred as sequential plans, from plans which are optimal in their number of levels, they are referred as parallel plans. Searching optimal sequential plans is generally considered harder than searching optimal parallel plans. Büttner and Rintanen have proposed a search procedure which computes plans whose numbers of levels are fixed and whose numbers of actions are minimal. This procedure is notably used to calculate optimal sequential plans, starting from an optimal parallel plan. In this paper we describe a similar approach, which we have developed from the planner FDP. The idea consists in maintaining two structures, the first one representing the parallel plan and the other representing the sequential plan, performing the choices simultaneously in both structures. The techniques which were developed in FDP to compute sequential plans or parallel plans enable failures detection in the two structures. Experimental results show that this approach is in some cases more efficient than FDP when searching optimal sequential plans
Answer Set Planning Under Action Costs
Recently, planning based on answer set programming has been proposed as an
approach towards realizing declarative planning systems. In this paper, we
present the language Kc, which extends the declarative planning language K by
action costs. Kc provides the notion of admissible and optimal plans, which are
plans whose overall action costs are within a given limit resp. minimum over
all plans (i.e., cheapest plans). As we demonstrate, this novel language allows
for expressing some nontrivial planning tasks in a declarative way.
Furthermore, it can be utilized for representing planning problems under other
optimality criteria, such as computing ``shortest'' plans (with the least
number of steps), and refinement combinations of cheapest and fastest plans. We
study complexity aspects of the language Kc and provide a transformation to
logic programs, such that planning problems are solved via answer set
programming. Furthermore, we report experimental results on selected problems.
Our experience is encouraging that answer set planning may be a valuable
approach to expressive planning systems in which intricate planning problems
can be naturally specified and solved
Logic programming for deliberative robotic task planning
Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture for robots that combines the
complementary strengths of probabilistic graphical models and declarative
programming to represent and reason with logic-based and probabilistic
descriptions of uncertainty and domain knowledge. An action language is
extended to support non-boolean fluents and non-deterministic causal laws. This
action language is used to describe tightly-coupled transition diagrams at two
levels of granularity, with a fine-resolution transition diagram defined as a
refinement of a coarse-resolution transition diagram of the domain. The
coarse-resolution system description, and a history that includes (prioritized)
defaults, are translated into an Answer Set Prolog (ASP) program. For any given
goal, inference in the ASP program provides a plan of abstract actions. To
implement each such abstract action, the robot automatically zooms to the part
of the fine-resolution transition diagram relevant to this action. A
probabilistic representation of the uncertainty in sensing and actuation is
then included in this zoomed fine-resolution system description, and used to
construct a partially observable Markov decision process (POMDP). The policy
obtained by solving the POMDP is invoked repeatedly to implement the abstract
action as a sequence of concrete actions, with the corresponding observations
being recorded in the coarse-resolution history and used for subsequent
reasoning. The architecture is evaluated in simulation and on a mobile robot
moving objects in an indoor domain, to show that it supports reasoning with
violation of defaults, noisy observations and unreliable actions, in complex
domains.Comment: 72 pages, 14 figure