We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper limit on the number of units that can be allocated in any period. We propose two versions on a truthful allocation mechanism. Each modifies the decisions of a greedy online assignment algorithm by sometimes cancelling an allocation of resources. One version makes this modification immediately upon an allocation decision while a second waits until the point at which an agent departs the market. Adopting a prior-free framework, we show that the second approach has better worst-case allocative efficiency and is more scalable. On the other hand, the first approach (with immediate cancellation) may be easier in practice because it does not need to reclaim units previously allocated. We consider an application to recharging plug-in hybrid electric vehicles (PHEVs). Using data from a real-world trial of PHEVs in the UK, we demonstrate higher system performance than a fixed price system, performance comparable with a standard, but non-truthful scheduling heuristic, and the ability to support 50% more vehicles at the same fuel cost than a simple randomized policy.

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.

We believe that these goals can be attained through the use of a very high level of modularity, both in hardware and software, combined with intelligent software. To test this hypothesis, Palo Alto Research Center (PARC) designed and built a prototype highly modular system in the printing domain. This "hypermodular" printer explores the extremes of modularity, reconfigurability, and parallelism in both hardware and software. The hardware prototype connects four standard Xerox marking engines (the component of a printer that does the actual printing) in parallel using a highly modular paper path. This configuration can achieve a print rate of four times that of an individual print engine. Reconfigurable manufacturing systems supports flexibility in configuration, graceful degradation (RMSs) were introduced as a concept in the late under component failure, and rerouting of inprocess 1990s (Koren et al. 1999), but the prerequisites, in sheets under exception conditions. These both software and hardware, for implementing them capabilities were made possible by utilizing advanced successfully have proved daunting; very few examples AI techniques in model-based planning, scheduling, of RMSs exist today in practice. These prerequisites search, and temporal reasoning such as state-space include modular, reconfigurable hardware components regression planning, partial-order scheduling, temporal as well as the software and control planning graph-based heuristic estimates, multiobjective architectures and logic to support them. RMSs can search, and fast, simple temporal network include both hard reconfigurability (physical reconfiguration) reasoning. The AI planner / scheduler incorporates and soft reconfigurability (logical reconfiguration) mostly domain-independent techniques from the (ElMaraghy 2006). This latter concept planning and scheduling research community, includes the idea of flexible routing as well as replanning enabling its flexibility and configurability to be and rescheduling.

Automated planning is the process of automatically selecting actions that achieve a desired outcome. This paper summarises several contributions that improve the efficiency of automated planning via heuristic search. We discuss novel heuristics based on landmarks and a search algorithm for anytime planning. Furthermore, we analyse various search-enhancement techniques and show how the combination of these techniques lead to a planning system that proved highly successful in the 2008 and 2011 International Planning Competitions.

Sustainable supply chain management has been an increasingly important topic of research in recent years. At the strategic level, there are computational models which study supply and distribution networks with environmental considerations. At the operational level, there are, for example, routing and scheduling models which are constrained by carbon emissions. Our paper explores work in tactical planning with regards to vehicle resource allocation from distribution centers to customer locations in a multi-echelon logistics network. We formulate the bi-objective optimization problem exactly and design a memetic algorithm to efficiently derive an approximate Pareto front. We illustrate the applicability of our approach with a large real-world dataset.

Most existing work on learning planning models assumes that the entire model needs to be learned from scratch. A more realistic situation is that the planning agent has an incomplete model which it needs to refine through learning. In this paper we propose and evaluate a method for doing this. Our method takes as input an incomplete model (with missing preconditions and effects in the actions), as well as a set of plan traces that are known to be correct. It outputs a refined model that not only captures additional precondition/effect knowledge about the given actions, but also macro actions. We use a MAX-SAT framework for learning, where the constraints are derived from the executability of the given plan traces, as well as the preconditions/effects of the given incomplete model. Unlike traditional macro-action learners which use macros to increase the efficiency of planning (in the context of a complete model), our motivation for learning macros is to increase the accuracy (robustness) of the plans generated with the refined model. We demonstrate the effectiveness of our approach through a systematic empirical evaluation.

There is increasing awareness in the planning community that the burden of specifying complete domain models is too high, which impedes the applicability of planning technology in many real-world domains. Although there have been many learning approaches that help automatically creating domain models, they all assume plan traces (training data) are \emph{correct}. In this paper, we aim to remove this assumption, allowing plan traces to be with noise. Compared to collecting large amount of correct plan traces, it is much easier to collect noisy plan traces, e.g., we can directly exploit sensors to help collect noisy plan traces. We consider a novel solution for this challenge that can learn action models from noisy plan traces. We create a set of random variables to capture the possible correct plan traces behind the observed noisy ones, and build a graphical model to describe the physics of the domain. We then learn the parameters of the graphical model and acquire the domain model based on the learnt parameters. In the experiment, we empirically show that our approach is effective in several planning domains.

AI planners have to compromise between the speed of the planning process and the quality of the generated plan. Anytime planners try to balance these objectives by finding plans of better quality over time, but current anytime planners often do not make effective use of increasing runtime beyond a certain limit. We present a new method of continuing plan improvement, that works by repeatedly decomposing a given plan into subplans and optimising each subplan locally. The decomposition exploits block-structured plan deordering to identify coherent subplans, which make sense to treat as units. This approach extends the "anytime capability" of current planners - to provide continuing plan quality improvement at any time scale.

One drawback of Hierarchical Task Network (HTN) planning is the difficulty of providing complete domain knowledge, i.e., a complete and correct set of HTN methods for every task. To provide a principled way to overcome this difficulty, we define a simple formalism that extends classical planning to include problem decomposition using methods, and a planning algorithm based on this formalism. In our formalism, the methods specify ways to achieve goals (rather than tasks as in conventional HTN planning), and goals may be achieved even when no methods are available. Our planning algorithm, GoDeL (Goal Decomposition with Landmarks), is sound and complete irrespective of whether the domain knowledge (i.e., the set of methods given to the planner) is complete. By comparing GoDeL's performance with varying amounts of domain knowledge across three benchmark planning domains, we show experimentally that (1) GoDeL works correctly with partial planning knowledge, (2) GoDeL's performance improves as more planning knowledge is given, and (3) when given full domain knowledge, GoDeL matches the performance of a state-of-the-art hierarchical planner.

Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning.