The PDDL+ language has been mainly devised to allow modelling of real-world systems, with continuous, time-dependant dynamics. Several interesting case studies with these characteristics have been also proposed, to test the language expressiveness and the capabilities of the support tools. However, most of these case studies have not been completely developed so far. In this paper we focus on the batch chemical plant case study, a very complex hybrid system with nonlinear dynamics that could represent a challenging benchmark problem for planning techniques and tools. We present a complete PDDL+ model for such system, and show an example application where the UPMurphi universal planner is used to generate a set of production policies for the plant.

Recently, "determinization in hindsight" has enjoyed surprising success in on-line probabilistic planning. This technique evaluates the actions available in the current state by using non-probabilistic planning in deterministic approximations of the original domain. Although the approach has proven itself effective in many challenging domains, it is computationally very expensive. In this paper, we present three significant improvements to help mitigate this expense. First, we use a method for detecting potentially useful actions, allowing us to avoid estimating the values of unnecessary ones. Second, we exploit determinism in the domain by reusing relevant plans rather than computing new ones. Third, we improve action evaluation by increasing the chance that at least one determin- istic plan reaches a goal. Taken together, these improvements allow determinization in hindsight to scale significantly better on large or mostly-deterministic problems.

Communications frequent criticism against this approach, however, is that verification Research Division, Institute for Defense Analysis, is done after significant resources have already been 1957.

The Simple Temporal Problem (STP) is a popular representation for solving centralized scheduling and planning problems. When scheduling agents are associated with different users who need to coordinate some of their activities, however, considerations such as privacy and scalability suggest solving the joint STP in a more distributed manner. Building on recent advances in STP algorithms that exploit loosely-coupled problem structure, this paper develops and evaluates algorithms for solving the multiagent STP. We define a partitioning of the multiagent STP with provable privacy guarantees, and show that our algorithms can exploit this partitioning while still finding the tightest consistent bounds on timepoints that must be coordinated across agents. We also demonstrate empirically that our algorithms can exploit concurrent computation, leading to solution time speed-ups over state-of-the-art centralized approaches, and enabling scalability to problems involving larger numbers of loosely-coupled agents.

International Conference on Automated Planning and Scheduling, held in Toronto, Ontario, For ICAPS 2010, we received 113 submissions Canada, May 12-16, 2010. The annual ICAPS from authors of 31 countries, representing all conference series was established in 2003 continents. From these submissions, 79 were through the merger of two preexisting biennial full papers, 29 were short ones, and 5 were position conferences, the International Conference on or challenge papers. These papers were all Artificial Intelligence Planning and Scheduling reviewed by a Program Committee made up of (AIPS) and the European Conference on Planning 76 members, coordinated by 10 Senior Members, (ECP). ICAPS continues the traditional and the four PC Chairs.

We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.

The objective of automated planners is to synthesize sequences of actions (called policies in MDP frameworks) that will achieve a predetermined goal given a fully or partially observable formal representation of the domain. In contrast, the main characteristic of project management is the greater emphasis on plan execution under uncertainty as opposed to plan synthesis. This paper explains the need to transition from automated plan synthesis to plan management and identifies the challenges for the planning and scheduling communities using examples of construction projects.

Evolutionary trees of species can be reconstructed by pairwise comparison of their entire genomes. Such a comparison can be quantified by determining the number of events that change the order of genes in a genome. Earlier Erdem and Tillier formulated the pairwise comparison of entire genomes as the problem of planning rearrangement events that transform one genome to the other. We reformulate this problem as a planning problem to extend its applicability to genomes with multiple copies of genes and with unequal gene content, and illustrate its applicability and effectiveness on three real datasets: mitochondrial genomes of Metazoa, chloroplast genomes of Campanulaceae, chloroplast genomes of various land plants and green algae.

We report (to our knowledge) the first evaluation of Constraint Satisfaction as a computational framework for solving closest string problems. We show that careful consideration of symbol occurrences can provide search heuristics that provide several orders of magnitude speedup at and above the optimal distance. We also report (to our knowledge) the first analysis and evaluation -- using any technique -- of the computational difficulties involved in the identification of all closest strings for a given input set. We describe algorithms for web-scale distributed solution of closest string problems, both purely based on AI backtrack search and also hybrid numeric-AI methods.

Electronic Geometry Textbook is a knowledge management system that manages geometric textbook knowledge to enable users to construct and share dynamic geometry textbooks interactively and efficiently. Based on a knowledge base organizing and storing the knowledge represented in specific languages, the system implements interfaces for maintaining the data representing that knowledge as well as relations among those data, for automatically generating readable documents for viewing or printing, and for automatically discovering the relations among knowledge data. An interface has been developed for users to create geometry textbooks with automatic checking, in real time, of the consistency of the structure of each resulting textbook. By integrating an external geometric theorem prover and an external dynamic geometry software package, the system offers the facilities for automatically proving theorems and generating dynamic figures in the created textbooks. This paper provides a comprehensive account of the current version of Electronic Geometry Textbook.