Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined

An investigation on the relationship between the user model and graphic representations for the automated generation of multimedia presentations.

This thesis investigates a possible solution to adapting an automatically generated presentation to an anonymous user. We will explore the field of User Modeling, specifically Adaptive Hypermedia, to find suitable methods. In our case study, we combine the methods we find to develop a concept for generating user-adapted multimedia presentations about the virtual collection of the Rijksmuseum of Amsterdam in their websit

A web-based Narrative construction environment

This paper describes a web-based environment for constructing narrative from story snippets contributed by a community of interest. The underlying model uses an argument based structure to infer the next event in the narrative sequence. The approach makes use of both events and higher level story elements derived from Polti’s dramatic situations. Dramatic situations used are consistent with a theme, and events are generally constrained by the dramatic situation. The narrative generated is a function of the event history, the dramatic situations chosen and the plausible inferences about next events that are contributed by a community of interest in the theme. At this stage, a player’s actions are simulated using a random selection from a set and the implementation of a nonsense filter. Example outputs from the system are provided and discussed

Scheduling of tests on vehicle prototypes

In the automotive industry, a manufacturer must perform several hundreds of tests on prototypes of a vehicle before starting its mass production. These tests must be allocated to suitable prototypes and ordered to satisfy temporal constraints and various kinds of test dependencies. To reduce costs, the manufacturer is interested in using the minimum number of prototypes. We apply Constraint Programming (CP) and a hybrid approach to solve the scheduling problem. Our CP method can achieve good feasible solutions even for our largest instances within a reasonable time. In comparison with existing methods, we can improve the solutions for most of our instances and reduce the average number of required prototypes. The hybrid approach uses mixed integer linear programming (MILP) to solve the planning part and CP to find the complete schedule. Although the hybrid approach is not as robust as CP with respect to data characteristics and additional constraints, it can complement CP in finding a better lower bound