Strategy Configuration and Selection for Automated Negotiation Agents

Negotiation is the art of resolving conflicts of interests by finding outcomes that all parties agree with. Humans negotiate often, as there are many conflicts of interest in everyday life when working with other humans. Computer scientists have build computer agents that are capable of negotiating with each other in an attempt to outperform or...

Towards Automated and Fast Whisker Tracking in Rodents

Whisker tracking in rodents is an ongoing research in neuroscience. Neuroscientists have recorded experiments with high-speed cameras in which untrimmed, head-restrained mice are provided with air stimuli. These videos required the development of algorithms to reliably track whisker movement. Recently, a Whisker Tracking System, WhiskEras,...

Automated Configuration and Usage of Strategy Portfolios for Mixed-Motive Bargaining

Bargaining can be used to resolve mixed-motive games in multiagent systems. Although there is an abundance of negotiation strategies implemented in automated negotiating agents, most agents are based on single fixed strategies, while it is acknowledged that there is no single best-performing strategy for all negotiation settings. In this...

Evolving a Search Procedure for Program Synthesis

In recent months, researchers developed several new search procedures to augment the process of program synthesis. While many of them performed better than their predecessors, the proposed solutions are still far from ideal. One possible way of overcoming the shortcomings of single search methods is employing genetic algorithms, which have been...

Optimal decision tree using dynamic programming: for the algorithm selection problem

Several algorithms can often be used to solve a complex problem, such as the SAT problem or the graph coloring problem. Those algorithms differ in terms of speed based on the size or other features of the problem. Some algorithms perform much faster on a small size while others perform noticeably better on a larger instance. The optimization...

Applying machine learning in route optimization: Predicting construction algorithm performance for the vehicle routing problem using neural networks

The real-life Vehicle Routing Problem (VRP) is the problem in which a set of vehicles needs to perform a set of tasks such that we have a shortest total driving distance. Such problems can be solved using construction algorithms. Finding the best-performing construction algorithm is time-consuming because these algorithms consist of many...

Optimal decision trees for the Algorithm Selection Problem: A dynamic programming approach

The Algorithm Selection Problem is a relevant question in computer science that would enable us to predict which algorithm would perform better on a given instance of a problem. <br/>Different solutions have been proposed, either using Mixed Integer Programming or machine learning models, but both suffer from either poor scalability, no...

Solving machine learning with machine learning: Exploiting Very Large-Scale Neighbourhood Search for synthesizing machine learning pipelines

This paper presents a comparative study of multiple algorithms that can be used to automatically search for high-performing pipelines on machine learning problems. These algorithms, namely Very Large-Scale Neighbourhood search (VLSN), Breadth-first search, Metropolis-Hastings, Monte-Carlo tree search (MCTS), enumerative A* search, and Genetic...