Abstract. In this paper we describe Constraint-based Attribute and Interval Planning (CAIP), a paradigm for representing and reasoning about plans. The paradigm enables the description of planning domains with time, resources, concurrent activities, mutual exclusions among sets of activities, disjunctive preconditions and conditional effects. We provide a theoretical foundation for the paradigm, based on temporal intervals and attributes. We then show how the plans are naturally expressed by networks of constraints, and show that the process of planning maps directly to dynamic constraint reasoning. In addition, we define compatibilities, a compact mechanism for describing planning domains. We describe how this framework can incorporate the use of constraint reasoning technology to improve planning. Finally, we describe EUROPA, an implementation of the CAIP framework. 1. What Should a Planner Do? In recent years, planning has been applied to complex domains, including the sequencing of commands for spacecraft both on the ground and on-board (Jónsson et al., 2000). The domain of spacecraft operations

In most real-world reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. On the other hand, most existing automated planners studied in Artificial Intelligence do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing state of the art planners which can be overcome by leveraging the loose coupling. Specifically, a novel planning framework called RealPlan is developed in which resource allocatio...

Stochastic local search (SLS) algorithms for propositional satisfiability testing (SAT) have become popular and powerful tools for solving suitably encoded hard combinatorial from different domains like, e.g., planning. Consequently, there is a considerable interest in finding SAT-encodings which facilitate the efficient application of SLS algorithms. In this work, we study how two encodings schemes for combinatorial problems, like the well-known Constraint Satisfaction or Hamilton Circuit Problem, affect SLS performance on the SAT-encoded instances. To explain the observed performance differences, we identify features of the induces search spaces which affect SLS performance. We furthermore present initial results of a comparitive analysis of the performance of the SAT-encoding and-solving approach versus that of native SLS algorithms directly applied to the unencoded problem instances. 1

Planning for the optimal attainment of requirements is an important early lifecycle activity. However, such planning is difficult when dealing with competing requirements, limited resources, and the incompleteness of information available at requirements time. A novel approach to requirements optimization is described. A requirements interaction model is executed to randomly sample the space of options. This produces a large amount of data, which is then condensed by a summarization tool. Theresultisasmalllistofcritical decisions (i.e., those most influential in leading towards the desired optimum). This focuses human experts’ attention on a relatively few decisions and makes them aware of major alternatives. This approach is iterative. Each iteration allows experts to select from among the major alternatives. In successive iterations the execution and summarization modules are run again, but each time further constrained by the decisions made in previous iteration. In the case study shown here, out of 99 yes/no decisions (approximately 10 30 possibilities), five iterations were sufficient to find and make the 30 key ones. 1.

Stochastic local search (SLS) algorithms have been successfully applied to hard combinatorial problems from different domains. One important feature of SLS algorithms is the fact that their run-time behavior is characterized by a random variable. Consequently, the detailed knowledge of the run-time distribution provides important information for the analysis of SLS algorithms. In this paper we investigate the empirical run-time distributions for several state-of-the-art stochastic local search algorithms for SAT and CSP. Using statistical analysis techniques, we show that on a variety of problems from both randomized distributions and encodings of the blocks world planning and graph coloring domains, the observed run-time behavior can be characterized by exponential distributions. As a first direct consequence of this result, we establish that these algorithms can be easily parallelized with optimal speedup.

Hierarchical task network and action-based planning approaches have traditionally been studied separately. In many domains, human expertise in the form of hierarchical reduction schemas exists, but is incomplete. In such domains, hybrid approaches that use both HTN and action-based planning techniques are needed. In this paper, we extend our previous work on refinement planning to include hierarchical planning. Specifically, we provide a generalized plan-space refinement that is capable of handling non-primitive actions. The generalization provides a principled way of handling partially hierarchical domains, while preserving systematicity, and respecting the user-intent inherent in the reduction schemas. Our general account also puts into perspective the many surface differences between the HTN and action-based planners, and could support the transfer of progress between HTN and action-based planning approaches. 1 Introduction Traditionally, classical planning probl...

We describe a methodology for representing and optimizing user preferences on plans. Our approach differs from previous work on plan optimization in that we employ a generalization of commonly occurring plan quality metrics, providing an expressive preference language. We introduce a domain independent algorithm for incrementally improving the quality of feasible plans with respect to preferences described in this language. Finally, we experimentally show that plan quality can be significantly increased with little additional modeling effort for each domain.

Most existing planners intertwine the refinement of a partial plan with search by pushing the individual refinements of a plan into different search branches. Although this approach reduces the cost of handling partial plans, it also often leads to search space explosion. In this paper, we consider the possibility of handling the refinements of a partial plan together (without splitting them into search space). This is facilitated by disjunctive partial plan representations that can compactly represent large sets of partial plans. Disjunctive representations have hitherto been shunned since they may increase the plan handling costs. We argue that performance improvements can be obtained despite these costs by the use of (a) constraint propagation techniques to simplify the disjunctive plans and (b) CSP/SAT techniques to extract solutions from them. We will support this view by showing that some recent promising refinement planners, such as the GRAPHPLAN algorithm [2], can be seen as deriving their power from disjunctive plan representations. We will also present a new planning algorithm, UCPOPD, which uses disjunctive representations over UCPOP [19] to improve performance. Finally, we will discuss the issues and tradeoffs involved in planning with disjunctive representations.

Abstract. We introduce a novel approach to the smart execution of scenario-based models of reactive systems, such as those resulting from the multi-modal inter-object language of live sequence charts (LSCs). Our approach finds multiple execution paths from a given state of the system, and allows the user to interactively traverse them. The method is based on translating the problem of finding a superstep of execution into a problem in the AI planning domain, and issuing a known planning algorithm, which we have had to modify and strengthen for our purposes. 1

. We investigate the benefit of Tabu Search for satisfiability (SAT) and constraint satisfaction problems (CSP) and compare it to the more frequently used random walk heuristic. We argue, that a more deterministic direction of search as done with Tabu Search is worth considering also for SAT and CSP. We give experimental evidence that Tabu Search can be used to efficiently guide local search procedures like GSAT and WSAT for SAT and the min conflicts heuristic for CSP. The algorithms are tested on randomly generated problems and hard graph coloring instances from the DIMACS benchmark test set. Additionally, we give some explanation on the value of Tabu Search. 1 Introduction Constraint satisfaction problems (CSPs) and satisfiability (SAT) are central problems in Artificial Intelligence. Many practical problems like machine vision, spatial and temporal reasoning, and scheduling can be represented as CSP. SAT plays a central role in many reasoning applications and in complexity theory. ...