The experience-driven perspective

Ultimately, content is generated for the player. But so far, our algorithms have not taken specific players into account. Creating computational models of a player’s behaviour, preferences, or skills is called player modelling. With a model of the player, we can create algorithms that create content specifically tailored to that player. The experience-driven perspective on procedural content generation provides a framework for content generation based on player modelling; one of the most important ways of doing this is to use a player model in the evaluation function for search-based PCG. This chapter discusses different ways of collecting and encoding data about the player, primarily player experience, and ways of modelling this data. It also gives examples of different ways in which such models can be used.peer-reviewe

Evaluating content generators

Evaluating your content generator is a very important task, but difficult to do well. Creating a game content generator in general is much easier than creating a good game content generator—but what is a “good” content generator? That depends very much on what you are trying to create and why. This chapter discusses the importance and the challenges of evaluating content generators, and more generally understanding a generator’s strengths and weaknesses and suitability for your goals. In particular, we discuss two different approaches to evaluating content generators: visualizing the expressive range of generators, and using questionnaires to understand the impact of your generator on the player. These methods could broadly be called top-down and bottom-up methods for evaluating generators.peer-reviewe

An optimal path selection using lion optimization routing protocol for mobile ad-hoc network

MANET is a set of nodes that communicate with each other directly or indirectly. The nodes in MANET can be moved freely. The dynamic nature of the network makes several challenges. One of the challenges in routing is to transfer the data from the start node (source) to the end node (destination). Routing suffers from several metrics such as power-consuming, delay, packet delivery ratio, etc. This paper proposed a new protocol called the Lion Optimization Routing protocol (LORP) based on the lion algorithm and AODV protocol. This protocol uses the Lion Optimization Algorithm to select the optimal path. Firstly, we use lion optimization to select the optimal path using the LOA maximization algorithm depending on three main metrics Power Efficiency, Throughput, and Packet Delivery Ratio. Secondly, we use the LOA minimization algorithm to select the optimal path using two metrics Delay and Short Path. In LOA Maximization algorithm metrics calculated and choose the max path value. The result of this protocol is compared with AODV, DSR, and ANTHOCNET

Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

A Progressive Approach to Content Generation

Abstract. PCG approaches are commonly categorised as constructive, generate-and-test or search-based. Each of these approaches has its distinctive advantages and drawbacks. In this paper, we propose an approach to Content Generation (CG) – in particular level generation – that combines the advantages of construc-tive and search-based approaches thus providing a fast, flexible and reliable way of generating diverse content of high quality. In our framework, CG is seen from a new perspective which differentiates between two main aspects of the game-play experience, namely the order of the in-game interactions and the associated level design. The framework first generates timelines following the search-based paradigm. Timelines are game-independent and they reflect the rhythmic feel of the levels. A progressive, constructive-based approach is then implemented to evaluate timelines by mapping them into level designs. The framework is applied for the generation of puzzles for the Cut the Rope game and the results in terms of performance, expressivity and controllability are characterised and discussed.

Vision-based landing of a simulated unmanned aerial vehicle with fast reinforcement learning

Landing is one of the difficult challenges for an unmanned aerial vehicle (UAV). In this paper, we propose a vision-based landing approach for an autonomous UAV using reinforcement learning (RL). The autonomous UAV learns the landing skill from scratch by interacting with the environment. The reinforcement learning algorithm explored and extended in this study is Least-Squares Policy Iteration (LSPI) to gain a fast learning process and a smooth landing trajectory. The proposed approach has been tested with a simulated quadrocopter in an extended version of the USARSim Unified System for Automation and Robot Simulation) environment. Results showed that LSPI learned the landing skill very quickly, requiring less than 142 trials

Performance Study on a Window Type Air Conditioner Condenser Using Alternative Refrigerant R407C

In this work, a performance study was achieved on air cooled condenser of an air-conditioning unit. An experimental investigation was carried out on two condenser designs aided by a controlled environmental zone, which was designed and constructed for the current study. The effect of ambient air temperature on the condenser performance was studied by varying the controlled zone air temperature from 30 to 50oC. The first design is a four circuits 5/16" tube diameter condenser and the second is an eight circuits design with the same diameter. The experimental results showed that, an increase in the ambient air temperature has a negative effect on COP due to the decrease in the overall heat rejected, yet has a positive effect on refrigerant side pressure drop