Using NEAT for Continuous Adaptation and Teamwork Formation in Pacman

Despite games often being used as a testbed for new computational intelligence techniques, the majority of artificial intelligence in commercial games is scripted. This means that the computer agents are non-adaptive and often inherently exploitable because of it. In this paper, we describe a learning system designed for team strategy development in a real time multi-agent domain. We test our system in the game of Pacman, evolving adaptive strategies for the ghosts in simulated real time against a competent Pacman player. Our agents (the ghosts) are controlled by neural networks, whose weights and structure are incrementally evolved via an implementation of the NEAT (Neuro-Evolution of Augmenting Topologies) algorithm. We demonstrate the design and successful implementation of this system by evolving a number of interesting and complex team strategies that outperform the ghosts\u27 strategies of the original arcade version of the game

Attack Resilience of the Evolving Scientific Collaboration Network

Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local (“microscopic”) level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level – fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks

The co-evolution of organizational value capture, value creation and sustainable advantage

Despite recent emphasis on intra-organizational issues, scholarship on organizations, management and strategy remains unduly reliant on economic models, such as the industrial organization (IO) market structure-based analysis. The focus of such models is on price-output determination by firms and the economy-wide efficient allocation of scarce resources under conditions of full knowledge and certainty. This limits their usefulness for students of organizations who have concerns that are simultaneously wider and also focused on organizations, as opposed to just markets. In this paper, we aim to provide an answer and framework for analysing the most fundamental, indeed existential, issue of organization studies and strategic management scholarship. This is whether and how the pursuit of value capture from economic agents who perceive that they possess appropriable value creating advantages, capabilities and action potential, can motivate the emergence of organizations and their strategies and actions intended to capture socially co-created value in conditions of real life . To do so, we explore (the co-evolution of) value capture and creation and (their relationship to) organizational sustainable advantage (SA). In particular, we delve into the nature, determinants and relationship between organizational value capture and creation and explore causal pathways, trade-offs and their co-evolution, as well as vehicles through which SA can be effected in an evolving and uncertain environment. We also discuss implications for managerial practice, limitations and future research opportunities.Value Creation, Value Capture, Sustainable Advantage, Co-evolution

Using graph theory to investigate the role of expertise on infrastructure evolution: A case study examining the game Factorio

Research into critical infrastructure network architecture design faces two significant challenges. First, real-world network performance data is often not available due to being proprietary. Secondly, many efforts focus on analyzing the structure of an infrastructure network at a single point in time, while real-world networks are constantly evolving. In this article, these two gaps (need for more data and for time-series data) are examined by utilizing a new data source: the video game Factorio. Factorio is a manufacturing simulator. Utilizing publicly available recordings of players’ networks in game, a shared end point, and completion time stamps allows the examination of different network strategies. The key research question examined in this work is how does network evolution change when comparing ten expert and ten novice designers? This article provides two key contributions. First, a qualitative and quantitative analysis of how ten different structural graph theory metrics evolve when comparing expert and novice designers is provided. The expert dataset has a narrower distribution, indicating common strategies, and focuses on critical path manufacturing early in the network’s evolution. The second contribution is a set of time-series network data that can be used for additional studies. By examining the differences in network evolution between experts and novices, this article performs a critical first step towards using in-situ graph theory metrics as a decision aid for designers during infrastructure evolution

Evolving more efficient digital circuits by allowing circuit layout evolution and multi-objective fitness

We use evolutionary search to design combinational logic circuits. The technique is based on evolving the functionality and connectivity of a rectangular array of logic cells whose dimension is defined by the circuit layout. The main idea of this approach is to improve quality of the circuits evolved by the GA by reducing the number of active gates used. We accomplish this by combining two ideas: 1) using multi-objective fitness function; 2) evolving circuit layout. It will be shown that using these two approaches allows us to increase the quality of evolved circuits. The circuits are evolved in two phases. Initially the genome fitness in given by the percentage of output bits that are correct. Once 100% functional circuits have been evolved, the number of gates actually used in the circuit is taken into account in the fitness function. This allows us to evolve circuits with 100% functionality and minimise the number of active gates in circuit structure. The population is initialised with heterogeneous circuit layouts and the circuit layout is allowed to vary during the evolutionary process. Evolving the circuit layout together with the function is one of the distinctive features of proposed approach. The experimental results show that allowing the circuit layout to be flexible is useful when we want to evolve circuits with the smallest number of gates used. We find that it is better to use a fixed circuit layout when the objective is to achieve the highest number of 100% functional circuits. The two-fitness strategy is most effective when we allow a large number of generations

Community Detection on Evolving Graphs

Clustering is a fundamental step in many information-retrieval and data-mining applications. Detecting clusters in graphs is also a key tool for finding the community structure in social and behavioral networks. In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph. Furthermore, there are often limitations on the frequency of such probes, either imposed explicitly by the online platform (e.g., in the case of crawling proprietary social networks like twitter) or implicitly because of resource limitations (e.g., in the case of crawling the web). In this paper, we study a model of clustering on evolving graphs that captures this aspect of the problem. Our model is based on the classical stochastic block model, which has been used to assess rigorously the quality of various static clustering methods. In our model, the algorithm is supposed to reconstruct the planted clustering, given the ability to query for small pieces of local information about the graph, at a limited rate. We design and analyze clustering algorithms that work in this model, and show asymptotically tight upper and lower bounds on their accuracy. Finally, we perform simulations, which demonstrate that our main asymptotic results hold true also in practice

Evolution: Complexity, uncertainty and innovation

Complexity science provides a general mathematical basis for evolutionary thinking. It makes us face the inherent, irreducible nature of uncertainty and the limits to knowledge and prediction. Complex, evolutionary systems work on the basis of on-going, continuous internal processes of exploration, experimentation and innovation at their underlying levels. This is acted upon by the level above, leading to a selection process on the lower levels and a probing of the stability of the level above. This could either be an organizational level above, or the potential market place. Models aimed at predicting system behaviour therefore consist of assumptions of constraints on the micro-level – and because of inertia or conformity may be approximately true for some unspecified time. However, systems without strong mechanisms of repression and conformity will evolve, innovate and change, creating new emergent structures, capabilities and characteristics. Systems with no individual freedom at their lower levels will have predictable behaviour in the short term – but will not survive in the long term. Creative, innovative, evolving systems, on the other hand, will more probably survive over longer times, but will not have predictable characteristics or behaviour. These minimal mechanisms are all that are required to explain (though not predict) the co-evolutionary processes occurring in markets, organizations, and indeed in emergent, evolutionary communities of practice. Some examples will be presented briefly