This document is a thesis on the subject of single-agent on-line path planning in continuous,unpredictable and highly dynamic environments. The problem is finding and traversing a collision-free path for a holonomic robot, without kinodynamic restrictions, moving in an environment with several unpredictably moving obstacles or adversaries. The availability of perfect information of the environment at all times is assumed. Several static and dynamic variants of the Rapidly Exploring Random Trees (RRT) algorithm are explored, as well as an evolutionary algorithm for planning in dynamic environments called the Evolutionary Planner/Navigator. A combination of both kinds of algorithms is proposed to overcome shortcomings in both, and then a combination of a RRT variant for initial planning and informed local search for navigation, plus a simple greedy heuristic for optimization. We show that this combination of simple techniques provides better responses to highly dynamic environments than the RRT extensions.

Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be very efficient in solving high dimensional problems. Even though several RRT variants have been proposed to tackle the dynamic replanning problem, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs as an initial solution, informed local search to fix unfeasible paths and a simple greedy optimizer. The algorithm is capable of recognizing when the local search is stuck, and subsequently restart the RRT. We show that this combination of simple techniques provides better responses to a highly dynamic environment than the dynamic RRT variants.

Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be efficient in solving high dimensional problems. Even though several RRT variants have been proposed for dynamic replanning, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs for initial planning and informed local search for navigation. We show that this combination of simple techniques provides better responses to highly dynamic environments than the RRT extensions.

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation's effectiveness and execution speed by incorporating two extra steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repairs are carried out by using the Prioritization to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvement in the Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with interative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

Planning means deciding on the future course of action based on predictions of what will happen when an activity is carried out in one way or the other. As we apply action planning to autonomous, sensor-guided mobile robots with manipulators or even to humanoid robots we need very realistic and detailed predictions of the behavior generated by a plan in order to improve the robot's performance substantially. In this paper we investigate the high-fidelity temporal projection of realistic robot behavior based on physics- and sensor-based simulation systems. We equip a simulator and interpreter with means to log simulated plan executions into a database. A logic-based query and inference mechanism then retrieves and reconstructs the necessary information from the database and translates the information into a first-order representation of robot plans and the behavior they generate.  The query language enables the robot planning system to infer the intentions, the beliefs, and the world state at any projected time.  It also allows the planning system to recognize, diagnose, and analyze various plan failures typical for performing everyday manipulation tasks.

Finite-state and memoryless controllers are simple action selection mechanisms widely used in domains such as video-games and mobile robotics.  Memoryless controllers stand for functions that map observations into actions, while finite-state controllers generalize memoryless ones with a finite amount of memory.  In contrast to the policies obtained from MDPs and POMDPs, finite-state controllers have two advantages: they are often extremely compact, involving a small number of controller states or none at all, and they are general, applying to many problems and not just one. A limitation of finite-state controllers is that they must be written by hand. In this work, we address this limitation, and develop a method for deriving finite-state controllers automatically from models. These models represent a class of contingent problems where actions are deterministic and some fluents are observable.  The problem of deriving a controller from such models is converted into a conformant planning problem that is solved using classical planners, taking advantage of a complete translation introduced recently.  The controllers derived in this way are 'general' in the sense that they do not solve the original problem only, but many variations as well, including changes in the size of the problem or in the uncertainty of the initial situation and action effects.  Experiments illustrating the derivation of such controllers are presented.

Many potential real-world planning applications are on the border of planning and scheduling. To handle the complex choices of actions and temporal and resource constraints of these problems we need to integrate planning and scheduling techniques. Here we propose a transition-based formulation of temporal planning problems, that enables us to represent features like deadlines, time windows, release times etc. in a simple way. We describe a CSP encoding of the transition-based formulation and its potential advantages in integrating planning and scheduling techniques.

The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans.  Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits the assumption that actions change the state of objects, and require objects to be in a certain state before they can be executed.  The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences.  In this paper we describe the implemented LOCM algorithm, and analyse its performance by its application to the induction of domain models for several domains. To evaluate the algorithm, we used random action sequences from existing models of domains, as well as solutions to past IPC problems.

Recently, Helmert and Geffner proposed the context-enhanced additive heuristic, where fact costs are evaluated relative to context states that arise from achieving first a pivot condition of each operator. As Helmert and Geffner pointed out, the method can be generalized to consider contexts arising from arbitrary precedence constraints over operator conditions instead. Herein, we provide such a generalization. We extend Helmert and Geffner's equations, and discuss a number of design choices that arise. Drawing on previous work on goal orderings, we design a family of methods for automatically generating precedence constraints. We run large-scale experiments, showing that the technique can help significantly, depending on the choice of precedence constraints. We shed some light on this by profiling the behavior of all possible precedence constraints, using a sampling technique.