Towards Believable Resource Gathering Behaviours in Real-time Strategy Games with a Memetic Ant Colony System

AbstractIn this paper, the resource gathering problem in real-time strategy (RTS) games, is modeled as a path-finding problem where game agents responsible for gathering resources, also known as harvesters, are only equipped with the knowledge of its immediate sur- roundings and must gather knowledge about the dynamics of the navigation graph that it resides on by sharing information and cooperating with other agents in the game environment. This paper proposed the conceptual modeling of a memetic ant colony system (MACS) for believable resource gathering in RTS games. In the proposed MACS, the harvester's path-finding and resource gathering knowledge captured are extracted and represented as memes, which are internally encoded as state transition rules (mem- otype), and externally expressed as ant pheromone on the graph edge (sociotype). Through the inter-play between the memetic evolution and ant colony, harvesters as memetic automatons spawned from an ant colony are able to acquire increasing level of capability in exploring complex dynamic game environment and gathering resources in an adaptive manner, producing consistent and impressive resource gathering behaviors

A study on memetic computation, with applications to capacitated vehicle routing problems

Memetic computation, a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving, represents one of the frontiers in the computational intelligence research, of today's optimization research. This dissertation takes an explorative attitude in the design of memetic computing frameworks and algorithms by leveraging the co-adaptive nature of meme complexes or memeplexes. The current research presented in this dissertation takes multi-memes and memeplexes as focal point of interest in the context of memetic computation and develop meme-centric computing frameworks for more effective problem-solving in the context of stochastic optimization of Capacitated Vehicle Routing Problems (CVRP) and Vehicle Routing Problem with Stochastic Demands (VRPSD). The presented research works follow a natural progression from multi-memes to memeplexes in simple and adaptive hybrids. Firstly, a simple hybrid with a multi-memes individual learning was developed to overcome the inherent risk associated with reliance on a single meme to be used for individual learning in memetic computation for solving CVRP. The proposed simple hybrid invests local search time budget among multiple memes with different search biases during individual learning, in order to reduce risks of failing on problems from different classes as well as make finding high quality solutions more likely. On this basis, an adaptive hybrid with the conceptual modeling of memeplexes was explored as a self-configuring methodology capable of learning and adapting multi-memes individual learning to the given problem of interests while the search progresses online. The connection topology network of the memeplex representation, credit assignment criteria for evaluating individual memes and meme co-adaptation, as well as the role of emergent memeplexes in the individual learning process were explored, with its application for solving CVRPs of diverse characteristics. Finally, a memeplex robust solution scheme which seeks to achieve greater level of adaptivity in problems with uncertainty was proposed and studied for VRPSD. Via robust solution search scheme, memeplex individual learning and gene-meme coevolutionary model, the scheme was demonstrated to be highly efficient and effective problem solver in environments with uncertainty such as VRPSD.DOCTOR OF PHILOSOPHY (SCE

A conceptual modeling of meme complexes in stochastic search

In science, gene provides the instruction for making proteins, while meme is the sociocultural equivalent of a gene containing instructions for carrying out behavior. Taking inspiration from nature, we model the memeplex in search as instructions that specify the coadapted meme complexes of individuals in their lifetime. In particular, this paper presents a study on the conceptual modeling of meme complexes or memeplexes for more effective problem solving in the context of modern stochastic optimization. The memeplex representation, credit assignment criteria for meme coadaptation, and the role of emergent memeplexes in the lifetime learning process of a memetic algorithm in search are presented. A coadapted memetic algorithm that takes the proposed conceptual modeling of memeplexes into actions to solve capacitated vehicle routing problems (CVRPs) of diverse characteristics is then designed. Results showed that adaptive memeplexes provide a means of creating highly robust, self-configuring, and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and metaheuristics, on a plethora of CVRP sets considered

Memetic Computation—Past, Present & Future [Research Frontier

From the word mimeme of Greek origin, Dawkins coined the term meme in his 1976 book on The Selfish Gene [1]. He defined it as being the basic unit of cultural transmission or imitation. These days, the monosyllabic word meme that is an analog of the word gene has since taken flight to become one of the most successful metaphorical ideologies in computational intelligence. The new science of memetics today represents the mind-universe analog to genetics in cultural evolution, stretching across the fields of anthropology, biology, cognition, psychology, sociology and socio-biology.Accepted versio

Autonomous flock brush for non-photorealistic rendering

Non-photorealistic rendering systems strive to create compelling stylized effects from realistic images. We present an interactive process using flocks of autonomous agents to model a painter's brush. As flocks of agents glide across the canvas like bristles on a paint brush, a stylized picture can be produced by carefully directing the path of movement. The agents leave behind a trail of color resulting in painterly or pencil sketch looking images

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD

Measurement Error Estimation for Capacitive Voltage Transformer by Insulation Parameters

Measurement errors of a capacitive voltage transformer (CVT) are relevant to its equivalent parameters for which its capacitive divider contributes the most. In daily operation, dielectric aging, moisture, dielectric breakdown, etc., it will exert mixing effects on a capacitive divider’s insulation characteristics, leading to fluctuation in equivalent parameters which result in the measurement error. This paper proposes an equivalent circuit model to represent a CVT which incorporates insulation characteristics of a capacitive divider. After software simulation and laboratory experiments, the relationship between measurement errors and insulation parameters is obtained. It indicates that variation of insulation parameters in a CVT will cause a reasonable measurement error. From field tests and calculation, equivalent capacitance mainly affects magnitude error, while dielectric loss mainly affects phase error. As capacitance changes 0.2%, magnitude error can reach −0.2%. As dielectric loss factor changes 0.2%, phase error can reach 5′. An increase of equivalent capacitance and dielectric loss factor in the high-voltage capacitor will cause a positive real power measurement error. An increase of equivalent capacitance and dielectric loss factor in the low-voltage capacitor will cause a negative real power measurement error

Research on the Effect of Karst on Foundation Pit Blasting and the Stiffness of Optimal Rock-Breaking Cement Mortar

The existence of karst cavities has an important impact on the safety of foundation pit excavation projects. It is of engineering guiding value to study the influence of karst cavities on the blasting process of foundation pits and how to optimize the stiffness of cement mortar to improve the blasting effect. Based on the karst foundation pit bench blasting project of Shenzhen Dayun Foundation Pit Project, this paper adopts the SPH-FEM coupling calculation method to study the influence of karst cavities, cavity-filling water and cavity-filling silt clay on the rock-blasting process of bench blasting. We analyzed the development process of blasting damage of rock when the stiffness of karst cavity grouting filling changes under the conditions of slightly weathered, moderately weathered and strongly weathered limestone. The calculation results show that the karst cavity near the blasthole changes the direction of the minimum resistance line, which leads to the release of blasting energy; the rock breaking effect is improved when the karst cavity is filled with water medium and clay medium. Under the three limestone conditions, after the karst cavity is pretreated by cement grouting, the increase in the stiffness of the cement mortar makes the rock damage area first increase and then decrease after the karst cavity implosion, and There is a critical cement mortar stiffness that makes the best rock breaking effect. The critical cement stiffness of micro-, medium- and strongly weathered limestone is 2.2%, 6.1% and 27% of the blasted rock mass, respectively, which makes the karst cavity wall stress reach the peak value, and the rock-breaking effect is the best at this time