In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Consequently, they are forced to find plans that satisfy only a subset of the goals. Solving such partial satisfaction planning (PSP) problems poses several challenges, including an increased emphasis on modeling and handling plan quality (in terms of action costs and goal utilities). Despite the ubiquity of such PSP problems, very little attention has been paid to them in the planning community. In this paper, we start by describing a spectrum of PSP problems and focus on one of the more general PSP problems, termed PSP NET BEN-EFIT. We develop three techniques, (i) one based on integer programming, called OptiPlan, (ii) the second based on regression planning with reachability heuristics, called AltAlt ps, and (iii) the third based on anytime heuristic search for a forward state-space heuristic planner, called Sapa ps. Our empirical studies with these planners show that the heuristic planners generate plans that are comparable to the quality of plans generated by OptiPlan, while incurring only a small fraction of the cost.

Many planning problems can be characterized as over-subscription problems in that goals have different utilities, actions have different costs and the planning system must choose a subset that will provide the greatest net benefit. This type of problems can not be solved by existing planning systems, where goals are assumed to have uniform utility, and the planner can terminate only when all of the goals are achieved. Existing methods for such problems use greedy approaches, which pre-select a subset of goals based on their estimated utility, and solve for those goals. Unfortunately, greedy methods, while efficient, can produce plans of arbitrarily low quality. In this paper, we introduce a more sophisticated heuristic search framework for over-subscription planning problems. In our framework, top-level goals are treated as soft-constraints and the search is guided by a relaxed-plan based heuristic that estimates the most beneficial set of goals from a given state. We implement this search framework in the context of Sapa, a forward state-space planner. We provide preliminary empirical results that demonstrate the effectiveness of our approach in comparison to a greedy approach. 1

Planning systems for real-world applications need the ability to handle concurrency and numeric fluents. Nevertheless, the predominant approach to cope with concurrency followed by the most successful participants in the latest International Planning Competitions (IPC) is still to find a sequential plan that is rescheduled in a post-processing step. We present Temporal Fast Downward (TFD), a planning system for temporal problems that is capable of finding low-makespan plans by performing a heuristic search in a temporal search space. We show how the context-enhanced additive heuristic can be successfully used for temporal planning and how it can be extended to numeric fluents. TFD often produces plans of high quality and, evaluated according to the rating scheme of the last IPC, outperforms all state-of-the-art temporal planning systems.

Dealing with numerical information is practically important in many real-world planning domains where the executability of an action can depend on certain numerical conditions, and the action effects can consume or renew some critical continuous resources, which in PDDL can be represented by numerical fluents. When a planning problem involves numerical fluents, the quality of the solutions can be expressed by an objective function that can take different plan quality criteria into account. We propose an incremental approach to automated planning with numerical fluents and multi-criteria objective functions for PDDL numerical planning problems. The techniques in this paper significantly extend the framework of planning with action graphs and local search implemented in the LPG planner. We define the numerical action graph (NA-graph) representation for numerical plans and we propose some new local search techniques using this representation, including a heuristic search neighborhood for NA-graphs, a heuristic evaluation function based on relaxed numerical plans, and an incremental method for plan quality optimization based on particular search restarts. Moreover, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach, and at analyzing its effectiveness in terms of fast computation of a valid plan and quality of the best plan that can be generated within a given CPU-time limit. Overall, the results show that our planner performs quite well compared to other state-of-the-art planners handling numerical fluents.

While even STRIPS planners must search for plans of unbounded length, temporal planners must also cope with the fact that actions may start at any point in time. Most temporal planners cope with this challenge by restricting action start times to a small set of decision epochs, because this enables search to be carried out in state-space and leverages powerful state-based reachability heuristics, originally developed for classical planning. Indeed, decision-epoch planners won the International Planning Competition’s Temporal Planning Track in 2002, 2004 and 2006. However, decision-epoch planners have a largely unrecognized weakness: they are incomplete. In order to characterize the cause of incompleteness, we identify the notion of required concurrency, which separates expressive temporal action languages from simple ones. We show that decisionepoch planners are only complete for languages in the simpler class, and we prove that the simple class is ‘equivalent ’ to STRIPS! Surprisingly, no problems with required concurrency have been included in the planning competitions. We conclude by designing a complete state-space temporal planning algorithm, which we hope will be able to achieve high performance by leveraging the heuristics that power decision epoch planners. 1

The treatment of exogenous events in planning is practically important in many realworld domains where the preconditions of certain plan actions are affected by such events. In this paper we focus on planning in temporal domains with exogenous events that happen at known times, imposing the constraint that certain actions in the plan must be executed during some predefined time windows. When actions have durations, handling such temporal constraints adds an extra difficulty to planning. We propose an approach to planning in these domains which integrates constraint-based temporal reasoning into a graph-based planning framework using local search. Our techniques are implemented in a planner that took part in the 4th International Planning Competition (IPC-4). A statistical analysis of the results of IPC-4 demonstrates the effectiveness of our approach in terms of both CPU-time and plan quality. Additional experiments show the good performance of the temporal reasoning techniques integrated into our planner. 1.

Abstract. Automated service composition is an important approach to automatically aggregate existing functionality. While different planning algorithms are applied in this area, heuristic search is currently not used. Lacking features like the creation of compositions with parallel or alternative control flow are preventing its application. The prospect of using heuristic search for composition with quality of service properties motivated the extension of existing heuristic search algorithms. In this paper we present a heuristic search algorithm for automated service composition. Based on the requirements for automated service composition, shortcomings of existing algorithms are identified, and solutions for them presented.

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.

We present a heuristic search approach to solve partial satisfaction planning (PSP) problems. In these problems, goals are modeled as soft constraints with utility values, and actions have costs. Goal utility represents the value of each goal to the user and action cost represents the total resource cost (e.g., time, fuel cost) needed to execute each action. The objective is to find the plan that maximizes the trade-off between the total achieved utility and the total incurred cost; we call this problem PSP NET BENEFIT. Previous approaches to solving this problem heuristically convert PSP NET BENEFIT into STRIPS planning with action cost by pre-selecting a subset of goals. In contrast, we provide a novel anytime search algorithm that handles soft goals directly. Our new search algorithm has an anytime property that keeps returning better quality solutions until the termination criteria are met. We have implemented this search algorithm, along with relaxed plan heuristics adapted to PSP NET BENEFIT problems, in a forward state-space planner called Sapa PS. An adaptation of Sapa PS, called Yochan PS, received a “distinguished performance” award in the “simple preferences” track of the 5 th International Planning Competition.

We propose a situation calculus formalization of action domains that include numeric-valued fluents (so-called additive or measure fluents) and concurrency. Our approach allows formalizing concurrent actions whose effects increment/decrement the value of additive fluents. For describing indirect effects, we employ mathematical equations in a manner that is inspired by recent work on causality and structural equations.