Abstract-Novel computing systems are increasingly being composed of large numbers of heterogeneous components, each with potentially different goals or local perspectives, and connected in networks which change over time. Management of such systems quickly becomes infeasible for humans. As such, future computing systems should be able to achieve advanced levels of autonomous behaviour. In this context, the system's ability to be self-aware and be able to self-express becomes important. This paper surveys definitions and current understanding of self-awareness and self-expression in biology and cognitive science. Subsequently, previous efforts to apply these concepts to computing systems are described. This has enabled the development of novel working definitions for selfawareness and self-expression within the context of computing systems.
Abstract. Both reactive and deliberative qualities are essential for a good action selection mechanism. We present a model that embodies a hybrid of two very different neural network architectures inside an animat: one that controls their high level deliberative behaviours, such as the selection of sub-goals, and one that provides reactive and navigational capabilities. Animats using this model are evolved in novel and dynamic environments, on complex tasks requiring deliberative behaviours: tasks that cannot be solved by reactive mechanisms alone and which would traditionally have their solutions formulated in terms of search-based planning. Significantly, no a priori information is given to the animats, making explicit forward search through state transitions impossible. The complexity of the problem means that animats must first learn to solve sub-goals without receiving any reward. Animats are shown increasingly complex versions of the task, with the results demonstrating, for the first time, incremental neuro-evolutionary learning on such tasks.
Abstract. Unlike the classic Trading Agent competition (tac), where participants enter trading strategies into a market, the tac Market Design Competition (cat) allows participants to create rules for their own double auction market and set fees for traders, which they embody in agents known as specialists. Although the generalisation properties of traders when the specialist (i.e., the market mechanism) is fixed have been assessed, generalisation properties of specialists have not. It is unclear whether and how a specialist might (intentionally or unintentionally) favour certain trading strategies. We present an empirical analysis of specialists' generalisation abilities in various trading environments. Our results show that specialists can be sensitive to a number of factors, including the other trading and specialist strategies in the environment.
Abstract. We present the problem of automatic co-niching in which potential suppliers of some product or service need to determine which offers to make to the marketplace at the same time as potential buyers need to determine which offers (if any) to purchase. Because both groups typically face incomplete or uncertain information needed for these decisions, participants in repeated market interactions engage in a learning process, making tentative decisions and adjusting these in the light of experiences they gain. Perhaps surprisingly, real markets typically then exhibit a form of parallel clustering: buyers cluster into segments of similar preferences and buyers into segments of similar offers. For computer scientists, the interesting question is whether such co-niching behaviours can be automated. We report on the first simulation experiments showing automated co-niching is possible using reinforcement learning in a multi-attribute product model. The work is of relevance to designers of online marketplaces, of computational resource allocation systems, and of automated software trading agents.
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