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We train an evaluation function for a multi-player Chess variant board game called Duchess, which is played between two teams of two or three players, and is similar to Chess but with extra pieces, larger board size and significantly greater branching factor. Leaf positions in the alpha-beta search tree are evaluated with a linear combination of features, whose values are trained by self-play using...
The design of algorithms for Game AI agents usually focuses on the single objective of winning, or maximizing a given score. Even if the heuristic that guides the search (for reinforcement learning or evolutionary approaches) is composed of several factors, these typically provide a single numeric value (reward or fitness, respectively) to be optimized. Multi-Objective approaches are an alternative...
In this paper, we present two greedy, myopic algorithms for solving the partially observable travelling salesman problem. Although not optimal from a decision-theoretic viewpoint, these strategies are shown to perform reasonably well under the uncertain conditions of the environment. The first algorithm is a strictly greedy algorithm and has no tunable parameters, whereas the second algorithm uses...
The classification of datasets with a skewed class distribution is an important problem in data mining. Evolutionary undersampling of the majority class has proved to be a successful approach to tackle this issue. Such a challenging task may become even more difficult when the number of the majority class examples is very big. In this scenario, the use of the evolutionary model becomes unpractical...
Episode pattern mining is a very powerful technique to get high-valued information for people to solve real-life cross-disciplinary problems, such as for the analysis of manufacturing, stock markets, weather records and so on. As data grows, the mining process must be re-triggered again and again to obtain the most updated information. However, periodically re-mining the full dataset is not cost-effective,...
Using mRNA-Seq and clinical data for 469 clear cell Renal Cell Carcinoma (ccRCC) samples from The Cancer Genome Atlas (TCGA), we develop a protocol to identify patients likely to have early recurrence of their disease. We first split the data into two sets, with 380 samples in the training set and 89 samples in the test set. Using the training set, we identify genes whose outlier status (high or low...
Robots of the future would be sophisticated enough to do all kinds of tasks for the human master, while operating a team of robots would ensure that the tasks are done as efficiently as possible. This paper presents a solution to a very common mission wherein a team of robots needs to visit a number of mission sights and carry some operation there. The sights may have their own preference of robots...
Evolutionary multi-objective optimization has been employed in studies concerning evolutionary robotics, and in particular for the evolution of neuro-controllers. To allow the simultaneous multi-objective evolution of topology and weights, tailored search algorithms should be developed. Here, a modification to the well-known NEAT algorithm is suggested. The proposed algorithm, which is termed NEAT-MODS,...
Increasing the level of autonomy facilitates a vehicle in performing long-range operations with minimum supervision. This paper shows that the ability of Autonomous Underwater Vehicles (AUVs) to fulfill mission objectives is directly influenced by route planning and task assignment system performance. This paper proposes an efficient task-assign route-planning model in a semi-dynamic network, where...
The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard problem. In this article we introduce two new Particle Swarm Optimization (PSO) algorithms designed to address this noisy, high-dimensional optimization...
We investigate a learning strategy for a swarm of autonomous robots. We identify the robots with cognitive agents and describe a model of naïve creatures learning to cross a highway. The creatures use a type of “observational social learning”, in which each creature learns from observing the outcomes of the other creatures that have crossed the highway. The learning outcomes are influenced by many...
The evolution of cognition is a relatively under-explored issue in cognitive science and evolution theory. The development and influence of evolutionary psychology in recent decades has stimulated interest in it just recently, but the methods applied largely remain bound to ethological observation and the theory-based use of evolutionary principles. Here we illustrate a new empirical approach to answering...
A general MOPSO algorithm was applied to ZDT1-4. Bias in the archive solutions was observed in the initialisation of the archive solutions. The bias continued until simulation end because a general MOPSO algorithm does not contain any explicit way to correct bias in its archive. Pareto dominance testing was discovered to be a main contributor to the bias. Bias was also introduced by the target's problem...
Kriging-based Global optimization has been proposed and extensively used for solving black-box optimization problems with expensive function evaluations. The performance of such algorithm relies heavily on the effectiveness of the infill criterion that is used to decide which point to evaluate next. Two common infill criteria are, the probability of improvement (PI) and the expected improvement (EI)...
Social networks are effective tools for analyzing many social topics in sociology. In the past few decades, a great deal of efforts have been made to study the balance property of social networks. This paper presents a novel bi-objective model for social network structural balance, and a multiobjective discrete particle swarm optimizer is used to optimize the bi-objective model. Each single run of...
In many-objective optimization, visualization of true Pareto front or obtained non-dominated solutions is difficult. A proper visualization tool must be able to show the location, range, shape, and distribution of obtained non-dominated solutions. However, existing commonly used visualization tools in many-objective optimization (e.g., parallel coordinates) fail to show the shape of the Pareto front...
In this paper we assess the performance of the classic NSGA-II algorithm when applied to a broad and realistic formulation of a bi-objective travel planning problem. Given a set of destinations and a travel time window, our goal is to find a Pareto set of detailed travel itineraries, which are both cost and time efficient. When the sequence of cities is fixed, the travel planning problem is commonly...
Real-time strategy (RTS) games hold many challenges in the creation of a game AI. One of those challenges is creating an effective plan for a given context. Competitive game AIs have struggled to adapt and create good plans to counter the opponent strategy. In this paper, a new scheduling model is proposed regarding planning problems on RTS games. This problem consists of solving a multi-objective...
Transportation costs constitute an important part in delivering goods to customers and have a significant influence on competitive advantage of a company. How to reduce transportation costs. Vehicle routing is a critical factor in reducing transportation costs. An effective scheme to manage fleets and determine vehicle routes for delivering goods is important for carriers to survive. In the existing...
Protein-ligand docking programs are valuable tools in the modern drug discovery process for predicting the complex structure of a small molecule ligand and the target protein. Often, the configurational search algorithm in the docking tool consists of global search and local search. The former is to explore widely for promising regions in the search space and the latter is to optimize a candidate...
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