The mere psychological presence of relationship partners was hypothesized to trigger interpersonal goals that are then pursued nonconsciously. Qualitative data suggested that people tend to pursue different interpersonal goals within different types of relationships (e.g., mother, best friend, coworker). In several studies, priming participants ’ relationship representations produced goal-directed behavior (achievement, helping, understanding) in line with the previously assessed goal content of those representations. These findings support the hypothesis that interpersonal goals are component features of relationship representations and that mere activation of those representations, even in the partner’s physical absence, causes the goals to become active and to guide behavior nonconsciously within the current situation. Many of people’s most strongly held goals, fears, and desires spring from their ongoing close relationships. Friends, family members, colleagues, and romantic partners are those whom people try hardest to understand, to whom they wish to grow closer, and from whom they seek to gain approval. Relationship partners are the elicitors of strong and influential motivations—motivations that alter people’s perceptions, change their emotions, and guide

From Reactive Behaviour to Adaptive Behaviour Motivational models for behaviour in animals and robots E. H. Spier, Balliol College, Trinity Term 1997 A thesis submitted for the degree of Doctor of Philosophy This thesis presents one possible way to design a control architecture that can be used to govern artificial animals. Such artefacts perform multiple-tasks and are expected to exist in a somewhat hostile environment -- they have to be adaptive. It also defends the position that automata, and animals, need not use reasoning to perform intelligent behaviour. Drawing from an ethological conception of motivation, a mathematical framework was described, computer simulations performed and preliminary work on a real robot discussed. It was shown that a reactive motivational algorithm performs better than alternatives that use simplistic models of the world, in a multiple resource foraging task. The reactive motivational framework was then extended to encompass instrumental behaviour as ...

The anticipatory classifier system (ACS) combines the learning classifier system framework with the learning theory of anticipatory behavioral control. The result is an evolutionary system that builds an environmental model and further applies reinforcement learning techniques to form an optimal behavioral policy in the model. After providing some background as well as outlining the objectives of the system, we explain in detail all involved current processes. Furthermore, we analyze the deficiency of overspecialization in the anticipatory learning process (ALP), the main learning mechanism in the ACS. Consequently, we introduce a genetic algorithm (GA) to the ACS that is meant for generalization of over-specialized classifiers. We show that it is possible to form a symbiosis between a directed specialization and a genetic generalization mechanism achieving a learning mechanism that evolves a complete, accurate, and compact description of a perceived environment. Results in three...

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This chapter is meant to give a concise introduction to the topic of this book. The study of anticipatory behavior is referring to behavior that is dependent on predictions, expectations, or beliefs about future states. Hereby, behavior includes actual decision making, internal decision making, internal preparatory mechanisms, as well as learning.

Learning Classifier Systems (LCS) are rule based Reinforcement Learning (RL) systems which use a generalization capability. In this paper, we highlight the differences between two kinds of LCSs. Some are used to directly perform RL while others latently learn a model of the interactions between the agent and its environment. Such a model can be used to speed up the core RL process. Thus, these two kinds of learning processes are complementary. We show here how the notion of generalization differs depending on whether the system anticipates (like ACS, Anticipatory Classifier System and YACS, Yet Another Classifier System) or not (like XCS). Moreover, we show some limitations of the formalism common to ACS and YACS, and propose a new system, called MACS (Modular Anticipatory Classifier System), which allows the latent learning process to take advantage of new regularities. We describe how the model can be used to perform active exploration and how this exploration may be aggregated with the policy resulting from the reinforcement learning process. The different algorithms are validated experimentally and some limitations in presence of uncertainties are highlighted.

A new and original trend in the Learning Classifier System (LCS) framework is focussed on latent learning. These new LCSs call upon classifiers with a [condition], an [action] and an [effect] part. In psychology, latent learning is defined as learning without getting any kind of reward. In the LCS framework, this process is in charge of discovering classifiers which are able to anticipate accurately the consequences of actions under some conditions. Accordingly, the latent learning process builds a model of the dynamics of the environment. This model can be used to improve the policy learning process. This paper describes YACS, a new LCS performing latent learning, and compares it with ACS.

In this paper we show that a phenomenon in animal learning theory (the outcome devaluation effect) for which there is dispute over whether explicit representations and symbolic reasoning is required for its performance, does not require such things. This is done using a reactive motivational model, previously inspired from ethological thought, to which some simple reinforcement learning rules are attached. An instantation of the model is used as the control system of an animat in a spatial computer simulation and it succeeds in learning the necessary parameters to allow the behaviour sequencing system to exhibit the phenomenon. 1 Introduction How complex can a reactive animat's behaviours get before some begin to appeal for a return to the well established rational techniques in classical artificial intelligence ? This paper offers an analysis and performance of a phenomenon in animal learning theory that provokes controversy about the type and complexity of the cognitive machinery ...

Abstract. This paper takes an overtly anticipatory stance to the understanding of animat learning and behavior. It analyses four major animal learning theories and attempts to identify the anticipatory and predictive elements inherent to them, and to provide a new unifying approach based on the predictive nature of those elements. Parallels are then drawn with Karl Popper’s “Logic of Scientific Discovery ” in order to show how an animat controller may be built inspired by those principles. The paper discusses the extent, and limitations, to this approach in an animat context and indicates how these principles were used to define the Dynamic Expectancy Model, and construct its implementation SRS/E. 1

An Anticipatory Classifier System (ACS) is a learning mechanism based on learning classifier systems and the cognitive model of "Anticipatory Behavioral Control". By comparing perceived consequences with its own expectations (anticipations), an ACS is able to learn in multi-step environments. To date, the ACS has proven its abilities in various problems of that kind. It is able to learn latently (i.e. to learn without getting any reward) and it is able to distinguish between non-Markov states. Additionally, an ACS is capable of incrementally building a cognitive map that can be used to do action-planning. Although the ACS has proven to scale up in suitable environments, it depends on certain environmental properties. It believes itself to be the only agent that can change the perceptions received from an environment. Any environmental change is considered and believed to be caused by the executed actions. The ACS learns from the changes by using fixed mechanisms. This paper reveals the properties of an environment that the current ACS assumes to be given. By investigating the problems of the current ACS when violating these properties we believe that this investigation will immediately serve for a better understanding of the ACS and lead to many ideas to improve the current ACS. We will propose some ideas and discuss the important ones in more detail.