This work presents a novel approach to training Reinforcement Learning (RL) agents to serve as heuristics for the WaveFunctionCollapse (WFC) algorithm in the production of procedurally generated video game levels. While the algorithm’s original minimal entropy heuristic is sufficient for constructing levels that look visually appealing, it is often the case that these levels suffer when playability is concerned. The approach presented in this work involves replacing this heuristic with a set of deep neural networks trained using RL to direct the algorithm in the construction of playable levels for the original Super Mario Bros. (SMB). We evaluate the performance of our models using a game-playing A*-based agent provided by the Mario AI Competition Framework and designate a simple reward function which reflects the quality of generated levels based on the A* agent’s ability to successfully navigate them. Results using this approach show an increase in the percentage of playable levels generated using our learned heuristics over those using minimal entropy.

The system that links intelligent agents to their world is their synthetic senses, allowing them to perceive and interact with the world around them. How such a system is modelled is important since an agent uses the data generated by its synthetic senses to make decisions and change behaviours. This paper will discuss a data-driven synthetic sight model for an autonomous agent that incorporates the concepts of human peripheral vision and visual acuity. We have developed and implemented a synthetic sight model that facilitates a good simulation of these physiological aspects of human sight.

Context-aware applications are able to use context, which refers to information about the surrounding environment, to provide relevant information and/or services to the user. A context-aware application may need to make use of existing services (e.g., a print service). There may be several possible choices of services. The context-aware application should be able to discover and select a service that considers context (e.g., current user location). Existing architectures and protocols for service discovery, however, are not suitable for doing so. Contextual information, by its very nature, is dynamic, reflecting the current state and conditions of the application, its user, or its operating environment. Existing architectures and protocols for service discovery, however, tend to assume the world is static, with attributes describing services offered never changing. If attributes are allowed to change, the approaches do not provide the architectural mechanisms required to update them; dynamic attributes with no means of updating are static for all intents and purposes. To support context-aware service discovery and selection, a better approach is required. This paper discusses one possible approach that is based on existing techniques.

It is often difficult to ensure that every application in a computing environment receives the level of quality of service required by their users. In such cases, the demand for computing resources to do so simply exceeds the limited supply that is available. To ensure that at least some user applications meet quality of service requirements, service differentiation is one approach growing in popularity. In this approach, preferential service is given to selected applications, while others deemed less important suffer in comparison. Most work in this area bases service differentiation decisions on static information about the applications, such as the name and type of application, the owner of the application, execution time, and the host on which the application was executed. In this paper, we discuss a new approach to service differentiation that takes into consideration dynamic application usage information in service differentiation decisions. In doing so, we can make better or fairer service differentiation decisions that allow more users to enjoy higher levels of quality of service. This is accomplished by ensuring preferential service is given to applications whose users can actually benefit from the improved service, as opposed to those applications that are essentially ignored or in a state that renders them unusable.

Management policies can be used to specify requirements about the desired behaviour of distributed systems. Violations of policies (faults) can then be detected, isolated, located and corrected using a policy-driven fault management system. Other work in this area to date has focused on network-level faults. We believe that in a distributed system it is more appropriate to focus on faults at the application level. Furthermore, this work has been largely domain-specific-a generic, structured approach to this problem is needed. Our work has focused on policy-driven fault management in distributed systems at the application level. In this paper, we define a generic architecture for policy-driven fault management and present a prototype system based on this architecture. We also discuss experience to date using and experimenting with our prototype system.