Autonomous multi-entity systems are plentiful in natural and artificial worlds. Many systems have been studied in depth and some models of them have been built as computational systems for problem solving. Central to these computational systems is the notion of autonomy. This article surveys research work done along this direction and proposes autonomy oriented computing (AOC) as a paradigm to describe systems for solving hard computational problems and for characterizing the behaviors of a complex system. AOC differs from major complex system related studies such as artificial life, simulated evolution, and multi-agent systems in that AOC is not just intended to replicate complex behavior, emulate evolution, or coordinate the functioning of many interacting agents. AOC emphasizes the modeling of autonomy in the entities of a complex system and the self-organization of them in achieving a specific goal. Through examining implemented applications, we identify three main approaches to AOC. Specifically, we provide a detailed description of the AOC framework with formal definitions of essential constructs and their interrelationships, including the notions of emergent autonomy, self-organization, and the interactions among entities and environment.
-based systems utilize autonomous entities that self-organize to achieve the goals of systems modeling and problem solving.N ature-inspired computing (NIC) is an emerging computing paradigm that draws on the principles of self-organization and complex systems. Here, we examine NIC from two perspectives. First, as a way to help explain, model, and characterize the underlying mechanism(s) of complex real-world systems by formulating computing models and testing hypotheses through controlled experimentation. The end product is a potentially deep understanding or at least a better explanation of the working mechanism(s) of the modeled system. And second, as a way to reproduce autonomous (such as lifelike) behavior in solving computing problems. With detailed knowledge of the underlying mechanism(s), simplified abstracted autonomous lifelike behavior can be used as a model in practically any general-purpose problem-solving strategy or technique.Neither objective is achievable without formulating a model of the factors underlying the system. The modeling process can begin with a theoretical analysis from either a macroscopic or microscopic view of the system. Alternatively, the application developer may adopt a blackbox or whitebox approach. Blackbox approaches (such as Markov models and artificial neural networks) normally do not reveal much about their working mechanism(s). On the other hand, whitebox approaches (such as agents with bounded rationality) are more useful for explaining behavior [7].The essence of NIC formulation involves conceiving a computing system operated by population(s) of autonomous entities. The rest of the system is referred to as the environment. An autonomous entity consists of a detector (or set of detectors), an effector (or set of effectors), and a repository of local behavior rules (see Figure 1) [5,8].A detector receives information related to its neighbors and to the environment. For example, in a simulation of a flock of birds, this information would include the speed and direction the birds are heading and the distance between the birds in question. The
With the advent of the WWW, providing justin-time personalized product recommendations to customers becomes possible. Collaborative recommender systems utilize the correlation between customer preference ratings to identify "like-minded" customers and predict their product preference. One factor determining the success of the recommender systems is the prediction accuracy, which in many cases is limited by lacking adequate ratings (the sparsity problem). Recently, the use of latent class model (LCM) has been proposed to alleviate this problem. In this paper, we first study how the LCM can be extended to handle customers and products outside the training set. In addition, we propose the use of a pair of LCMs (called dual latent class model-DLCM), instead of a single LCM, to model customers' likes and dislikes separately so as to enhance the prediction accuracy. Experimental results based on the EachMovie dataset show that DLCM outperforms both LCM and the conventional correlation-based method when the available ratings are sparse.
This article describes a new programming paradigm called autonomy‐oriented computing (AOC), which describes the construct of synthetic autonomy in locally interacting entities, and use the aggregated effects of entity interactions to generate desired global solutions or systems dynamics. The fundamental working mechanism of self‐organization that underlies the AOC paradigm offers the advantages of natural formulation as well as scalable performance to characterize complex systems or to computationally hard problems that are distributed and large scale in nature.
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