On the inference and management of macro-actions in forward-chaining planning

In this paper we discuss techniques for online generation of macro-actions as part of the planning process and demonstrate their use in a forward chaining search planning framework. The macroactions learnt are specifically created at places in the search space where the heuristic is not informative. We present results to show that using macro-actions generated during planning can improve planning performance

Simulating the use of macro-actions through action reordering

The use of macro-actions in planning introduces a trade-off.. Macro-actions can offer search guidance by suggesting sequences of actions; but can potentially make search more expensive by increasing the branching factor. In this paper we present a technique for simulating the use of macro actions by altering the order in which actions are considered for application during enforced hill-climbing search. Actions are ordered based on the number of times they have occurred, in past solution plans, following the last action added to the plan. We demonstrate that the action-reordering technique used can offer improved search performance without the negative performance impacts often observed when using macro-actions

A Delay-Aware Caching Algorithm for Wireless D2D Caching Networks

Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobile users, users can locate some of their requested files in their own caches or the caches at their neighbors. In the latter case, when a user receives files from its neighbors, device-to-device (D2D) communication is enabled. D2D communication underlaid with cellular networks is also a new paradigm for the upcoming 5G wireless systems. By allowing a pair of adjacent D2D users to communicate directly, D2D communication can achieve higher throughput, better energy efficiency and lower traffic delay. In this work, we propose a very efficient caching algorithm for D2D-enabled cellular networks to minimize the average transmission delay. Instead of searching over all possible solutions, our algorithm finds out the best pairs, which provide the best delay improvement in each loop to form a caching policy with very low transmission delay and high throughput. This algorithm is also extended to address a more general scenario, in which the distributions of fading coefficients and values of system parameters potentially change over time. Via numerical results, the superiority of the proposed algorithm is verified by comparing it with a naive algorithm, in which all users simply cache their favorite files

Extending the use of plateau-escaping macro-actions in planning

Many fully automated planning systems use a single, domain independent heuristic to guide search and no other problem specific guidance. While these systems exhibit excellent performance, they are often out-performed by systems which are either given extra human-encoded search information, or spend time learning additional search control information offline. The benefit of systems which do not require human intervention is that they are much closer to the ideal of autonomy. This document discusses a system which learns additional control knowledge, in the form of macro-actions, during planning, without the additional time required for an online learning step. The results of various techniques for managing the collection of macro-actions generated are also discussed. Finally, an explanation of the extension of the techniques to other planning systems is presented