Rune Møller Jensen

OBDD-based universal planning for synchronized agents in non-deterministic domains

Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDS) to encode a planning domain as a non-deterministic finite automaton and then apply fast algorithms from model checking to search for a solution. OBDDS can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this article, we present UMOP, a new universal OBDD-based planning framework for non-deterministic, multi-agent domains. We introduce a new planning domain description language, NADL, to specify non-deterministic, multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms. In addition, we introduce our new optimistic planning algorithm that relaxes optimality guarantees and generates plausible universal plans in some domains where no strong nor strong cyclic solution exists. We present empirical results applying UMOP to domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. UMOP is shown to be a rich and efficient planning system.

Is visual attention automatically attracted by one’s own name?

Subjects were presented with briefly exposed visual displays of words that were common first names with a length of four to six letters. In the main experiment, each display consisted of four words: two names shown in red and two shown in white. The subject’s task was to report the red names (targets), but ignore the white ones (distractors). On some trials the subject’s own name appeared as a display item (target or distractor). Presentation of the subject’s name as a distractor caused no more interference with report of targets than did presentation of other names as distractors. Apparently, visual attention was not automatically attracted by the subject’s own name.

A Constraint Programming model for fast optimal stowage of container vessel bays

Container vessel stowage planning is a hard combinatorial optimization problem with both high economic and environmental impact. We have developed an approach that often is able to generate near-optimal plans for large container vessels within a few minutes. It decomposes the problem into a master planning phase that distributes the containers to bay sections and a slot planning phase that assigns containers of each bay section to slots. In this paper, we focus on the slot planning phase of this approach and present a Constraint Programming and Integer Programming model for stowing a set of containers in a single bay section. This so-called slot planning problem is NP-hard and often involves stowing several hundred containers. Using state-of-the-art constraint solvers and modeling techniques, however, we were able to solve 90% of 236 real instances from our industrial collaborator to optimality within 1second. Thus, somewhat to our surprise, it is possible to solve most of these problems optimally within the time required for practical application.

Fast generation of near-optimal plans for eco-efficient stowage of large container vessels

Eco-efficient stowage plans that are both competitive and sustainable have become a priority for the shipping industry. Stowage planning is NP-hard and is a challenging optimization problem in practice. We propose a new 2-phase approach that generates near-optimal stowage plans and fulfills industrial time and quality requirements. Our approach combines an integer programming model for assigning groups of containers to storage areas of the vessel over multiple ports, and a constraint programming and local search procedure for stowing individual containers.

Phase recognition during surgical procedures using embedded and body-worn sensors

In Ubiquitous Computing (Ubicomp) research, substantial work has been directed towards sensor-based detection and recognition of human activity. This research has, however, mainly been focused on activities of daily living of a single person. This paper presents a sensor platform and a machine learning approach to sense and detect phases of a surgical operation. Automatic detection of the progress of work inside an operating room has several important applications, including coordination, patient safety, and context-aware information retrieval. We verify the platform during a surgical simulation. Recognition of the main phases of an operation was done with a high degree of accuracy. Through further analysis, we were able to reveal which sensors provide the most significant input. This can be used in subsequent design of systems for use during real surgeries.

OBDD-based universal planning: specifying and solving planning problems for synchronized agents in non-deterministic domains

Recently model checking representation and search techniques were shown to be efficiently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDS) to encode a planning domain as a non-deterministic finite automaton (NFA) and then apply fast algorithms from model checking to search for a solution. OBDDS can effectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this chapter, we present UMOP,1 a new universal OBDD-based planning framework for non-deterministic, multi-agent domains, which is also applicable to deterministic singleagent domains as a special case. We introduce a new planning domain description language, NADL,2 to specify non-deterministic multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms [9,10]. In addition, we introduce our new optimistic planning algorithm, which relaxes optimality guarantees and generates plausible universal plans in some domains where no strong or strong cyclic solution exist. We present empirical results from domains ranging from deterministic and single-agent with no environment actions to nondeterministic and multi-agent with complex environment actions. UMOP is shown to be a rich and efficient planning system.

CLab: a C++ library for fast backtrack-free interactive product configuration

Product configuration is a successful application area of constraint programming. CLab [1,2] is an open source C++ library for building fast backtrack-free interactive product configurators. It contains functions that support a two-phase approach to interactive product configuration described by Hadzic et al. [3]. In the first phase, a Binary Decision Diagram (BDD) representing the set of valid configurations is compiled offline. In the second phase, this BDD is accessed by the online interactive product configurator. The library has two major functions: one that builds the BDD from a declarative product model (M1), and one that computes the set of possible ways a current partial configuration can be extended to a valid product (M2). The latter function is fast (polynomial) and used to make the interactive product configuration process complete and backtrack-free. It allows the user to choose freely between any possible continuation of the partial configuration.

Generating optimal stowage plans for container vessel bays

Millions of containers are stowed every week with goods worth billions of dollars, but container vessel stowage is an all but neglected combinatorial optimization problem. In this paper, we introduce a model for stowing containers in a vessel bay which is the result of probably the longest collaboration to date with a liner shipping company on automated stowage planning. We then show how to solve this model efficiently in - to our knowledge - the first application of CP to stowage planning using state-of-the-art techniques such as extensive use of global constraints, viewpoints, static and dynamic symmetry breaking, decomposed branching strategies, and early failure detection. Our CP approach outperforms an integer programming and column generation approach in a preliminary study. Since a complete model of this problem includes even more logical constraints, we believe that stowage planning is a new application area for CP with a high impact potential.

On the complexity of container stowage planning problems

The optimization of container ship and depot operations embeds the k-shift problem, in which containers must be stowed in stacks such that at most k containers must be removed in order to reach containers below them. We first solve an open problem introduced by Avriel et al. (2000) by showing that changing from uncapacitated to capacitated stacks reduces the complexity of this problem from NP-complete to polynomial. We then examine the complexity of the current state-of-the-art abstraction of container ship stowage planning, wherein containers and slots are grouped together. To do this, we define the hatch overstow problem, in which a set of containers are placed on top of the hatches of a container ship such that the number of containers that are stowed on hatches that must be accessed is minimized. We show that this problem is NP-complete by a reduction from the set-covering problem, which means that even abstract formulation of container ship stowage planning is intractable.

State-set branching: Leveraging BDDs for heuristic search

In this article, we present a framework called state-set branching that combines symbolic search based on reduced ordered Binary Decision Diagrams (BDDs) with best-first search, such as A* and greedy best-first search. The framework relies on an extension of these algorithms from expanding a single state in each iteration to expanding a set of states. We prove that it is generally sound and optimal for two A* implementations and show how a new BDD technique called branching partitioning can be used to efficiently expand sets of states. The framework is general. It applies to any heuristic function, evaluation function, and transition cost function defined over a finite domain. Moreover, branching partitioning applies to both disjunctive and conjunctive transition relation partitioning. An extensive experimental evaluation of the two A* implementations proves state-set branching to be a powerful framework. The algorithms outperform the ordinary A* algorithm in almost all domains. In addition, they can improve the complexity of A* exponentially and often dominate both A* and blind BDD-based search by several orders of magnitude. Moreover, they have substantially better performance than BDDA*, the currently most efficient BDD-based implementation of A*.