The state description and corresponding dynamic Bayesian network are shown in Table 1 and Figure 1. In the paper we mentioned that ms denotes the state of task i. To make this more detailed, we factorms into a tuple (m i r,m i d,m i s,m i c). The variable mr encodes the release time of task i. The variable md represents the number of steps task i can still be postponed, which can be used to enc...

Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a solution to learn on-line macro-actions, we propose an algorithm to identify useful macro-actions based on data mining techniques. The integration in the planning search of these lear...

This short paper (cf. [5]) proposes a planner that uses BDDs to compactly represent sets of propositionally represented states. Using this representation, accurate reachability analysis and backward chaining can apparently be carried out without necessarily encountering exponential representation explosion. The main objectives are the interest in optimal solutions, the generality and the concis...

Automating the operations of infrastructure networks such as energy grids and oil pipelines requires a range of planning and optimisation technologies. However, current planners face significant challenges in responding to this need. Notably, they are unable to model and reason about the global numerical state constraints necessary to capture flows and similar physical phenomena occurring in th...

Generative Process Planning systems provide a powerful means of generating manufacturing process plans from a product model. However, the vast majority of current commercial and experimental systems are limited in several ways: they tend to be specialized for metal-machining processes, they tend to focus on one state of the product at a time, and in general they lack an intelligent product mode...

We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Partially Observable Markov Decision Process (POMDP). However, it is well known that exactly solving POMDPs is very costly computationally. Recently, Littman, Sutton and Singh (2002) have proposed an alternative representa...

Due to a high penetration of renewable energy, power systems operational planning today needs to capture unprecedented uncertainties in a short period. Fast probabilistic state estimation (SE), which creates probabilistic load flow estimates, represents one such planning tool. This paper describes a graphical model for probabilistic SE modeling that captures both the uncertainties and the power...