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In this study we show how simulated robots evolved to display a navigation skills can spontaneously develop an internal model and rely on it to complete their task when sensory stimulation is temporarily unavailable. The analysis of some of the best evolved agents indicates that their internal model operates by anticipating functional properties of the next sensory state rather than the exact state that sensors would have assumed. The characteristics of the states that are anticipated and of the sensory-motor rules that determine how the agents react to the experienced states, however, ensure that the agents produce very similar behaviour during normal and blind phases in which sensory stimulation is available or is self-generated by the agent itself, respectively. The characteristics of the agents΄ internal models also ensure an effective transition during the phases in which agents΄ internal dynamics is decoupled and re-coupled with the sensory-motor flow. [ABSTRACT FROM AUTHOR]

One of the main claims of the active vision framework is that finding data on the basis of task requirements is more efficient than reconstructing the whole scene by performing a complete visual scan. To be successful, this approach requires that agents learn visual routines to direct overt attention to locations with the information needed to accomplish the task. In ecological conditions, learning such visual routines is difficult due to the partial observability of the world, the changes in the environment, and the fact that learning signals might be indirect. This paper uses a reinforcement-learning actor-critic model to study how visual routines can be formed, and then adapted when the environment changes, in a system endowed with a controllable gaze and reaching capabilities. The tests of the model show that: (a) the autonomously-developed visual routines are strongly dependent on the task and the statistical properties of the environment; (b) when the statistics of the environment change, the performance of the system remains rather stable thanks to the re-use of previously discovered visual routines while the visual exploration policy remains for long time sub-optimal. We conclude that the model has a robust behaviour but the acquisition of an optimal visual exploration policy is particularly hard given its complex dependence on statistical properties of the environment, showing another of the difficulties that adaptive active vision agents must face. [ABSTRACT FROM AUTHOR]

This book continues the enhanced post-workshop proceedings series on ˵Anticipatory Behavior in Adaptive Learning System″ (ABiALS), published as Springer LNAI 2684 and LNAI 4520 [3,5]. The proceedings offer a multidisciplinary perspective on anticipatory mechanisms in cognitive, social, learning, and behavioral processes, with contributions from key researchers in psychology and computer science. This introduction offers a conceptual terminology on anticipatory mechanisms and involved predictive capabilities. Moreover, it provides an overview of the book contributions, highlighting some of their peculiarities and complementarities. [ABSTRACT FROM AUTHOR]

This book has provided various theoretical perspectives on anticipatory processes in natural and artificial cognitive systems. Advantages have been proposed and confirmed in various detailed case studies, which may have given the reader detailed insights into anticipatory processes and their importance in various cognitive systems tasks. To wrap up these advantages and give a concluding overview of various current anticipatory process advantages, this final chapter highlights a concise collection of precise success stories of anticipations in artificial cognitive systems. We survey fourteen case studies, which were developed during the EU project MindRACES. In these studies, simulated or real robots were tested in different environmental tasks, which required advanced sensorimotor and cognitive abilities. These abilities included the initiation and control of goal-directed actions, the orientation of attention, finding and reaching goal locations, and performing mental experiments for action selection. All the studies have shown advantages of anticipatory mechanisms compared to reactive mechanisms in terms of increased robot autonomy and adaptivity. In some cases, anticipations even caused the development of new cognitive abilities, which were simply impossible without anticipatory mechanisms. For each case study, we indicate relevant associated publications, in which the interested reader may find further details on the relevant computational architectures, the involved anticipatory mechanisms, as well as on the analytical and quantitative results. While the book as a whole has laid out the theoretical principles and design methodology for such advancements, this final chapter thus provides various possible starting points for further developments in both the surveyed system architectures and the presented solutions to the cognitive tasks addressed. [ABSTRACT FROM AUTHOR]

David Hume may be one of the first who thought about the causes that actually enable us to act goal-directedly in our pursuit of happiness. Besides having usually an end, or goal, in mind, Hume realized that the end must elicit those means that were learned to correlate with the end. Such correlation knowledge, according to Hume (1748), was based on three types of connecting ˵ideas″: resemblance, contiguity in time and place, and cause and effect. Knowledge of correlations and cause-effect relations alone, though, do not directly lead to effective behavior. Thus, not only the question how we learn correlations in the environment needs to be addressed, but also how we can exploit the obtained knowledge, if learned properly. While Hume did mainly address the former question, the latter question was acknowledged by Hume only in so far that the acquired knowledge may be used to pursue our goals. Another related line of research on causation is put forward by Kant (1998). Sloman (2006) contrasted a `Humean΄ and a ΄Kantian΄ view of understanding correlations and causations in particular. The former is evidence-based, probabilistic, and statistical. The latter is structure-based and deterministic. Kant highlights the role of concepts and necessity in contrast with the Humean emphasis on observation and correlation. The Kantian notion of causation is more complex and requires understanding of spatial structures and relationships as well as the capability to reason about what happens when they change. While humans are usually seen as explorers that learn correlations in the world based on experimentation–and thus more ΄Humean΄ evidence-based–they rely on and inevitably detect the ΄Kantian΄ a priori structures in our world given due to time, space, and physical constraints. Just like humans, all other anticipatory systems, both biological and artificial, need to learn and store knowledge about themselves and the world. Only this knowledge enables them to predict future events, gauge the consequences of one΄s own actions, and finally interact competently and intelligently with objects or other agents. Thus, one of the questions addressed in this chapter is how knowledge about the world or the self may be represented. However, regardless if we acquire knowledge based on ΄Humean΄ or ΄Kantian΄ principles, or both, just having gathered knowledge about the world does not mean per se that our decisions will be wise and our actions will be appropriate. It is not even clear how predictive knowledge may be turned into actual decisions in general. The ideomotor principle, which dates back to the 19-th century (James 1890; Herbart 1825; cf. Hoffmann et al. 2004), suggests that actions are bi-directionally linked to the effects they usually produce. Thus, once a goal is chosen and activated, the bi-directional links point to those actions that previously caused the goal to come about. While this still does not clarify the actual mechanism of selecting the appropriate means, it implies that an inverse mechanism is necessary that stores means to achieve current goals. With the ideomotor principle as the basic principle of goal-directed behavior in mind, this chapter analyzes related predictive and anticipatory systems that learn predictive representations of their environment and can use those to act goal-directedly. Predictive systems are systems that are able to predict sensory inputs or pre-processed, more abstracted perceptual input. From an adaptive behavioral perspective most important are systems that learn such predictive representation. These predictive capabilities are an important part of any goal-directed behavioral system that is explicitly anticipatory. However, as suggested in the comparison of the insights put forward by David Hume and the ideomotor principle, predictive capabilities are only the f [ABSTRACT FROM AUTHOR]

This book proposes the anticipatory approach for the design of cognitive agents. That is, we propose to implement anticipatory mechanisms in artificial cognitive systems to generate highly flexible and adaptive systems that can efficiently cope with dynamic environments. While the first two chapters identified several different facets of anticipatory mechanisms, this chapter focuses on the benefits and potential drawbacks of using these types of anticipatory mechanisms in cognitive systems. We focus hereby on the explicit forms of anticipation defined in the previous chapter. Thus, this chapter intends to answer questions such as: ˵What are the specific advantages of anticipatory mechanisms and capabilities?″, or ˵Why should we endow artificial cognitive agents with anticipation?″, as well as, ˵Are there any disadvantages in doing so?″ This chapter first identifies three general potential beneficial aspects of anticipations and then provides concrete examples of the impact of anticipation in several cognitive functions such as motor control, learning, attention, and social interaction. Section 3.3 points out that anticipations might not always be advantageous and may sometimes interfere with effective behavioral execution–especially when the involved predictions are inaccurate or not synchronized well. Finally, we summarize and draw conclusions. [ABSTRACT FROM AUTHOR]

The anticipatory approach that we propose consists in understanding and conceptualizing anticipation and anticipatory behavior in natural cognition and implementing them in artificial systems. We propose that anticipatory systems have capabilities that go far beyond those of purely reactive ones and that anticipation is a prerequisite for several cognitive functions, and in general for goal-oriented behavior. Consequently, in this chapter we first provide definitions for anticipation and anticipatory behavior (Section 2.1). We then classify and distinguish different types and aspects of predictive and anticipatory mechanisms (Section 2.2). Next, we classify anticipatory mechanisms in goal-oriented behavior (Section 2.3) and finally distinguish anticipatory mechanisms in learning structures (Section 2.4). The classifications put forward in this chapter are meant to clarify what anticipatory mechanisms are and how they can influence cognitive systems΄ behavior and learning. Chapter 3 then reviews the potential benefits of anticipatory mechanism and also discusses potential drawbacks. In the subsequent chapters, we then take the artificial systems perspective and put forward the requirements and the capabilities of currently available anticipatory cognitive systems. [ABSTRACT FROM AUTHOR]

What will artificial cognitive systems of the future look like? If we are asked to imagine robots, or intelligent software agents, several features come to our mind such as the capability to adapt to their environments and to satisfy their goals with only limited human intervention, to plan sequences of actions for realizing long-term objectives, to act collectively in view of complex objectives, to interact and cooperate with us, with and without natural language, to take decisions (also in our place), etc. Currently these capabilities are far beyond the possibilities of robots and other artificial systems. In the next years a huge effort will be required for scaling up the potentialities of the artificial systems that we are able to build nowadays. One way to overcome these limitations is to take inspiration form the functioning of living organisms. A large body of evidence, which we review in this chapter, indicates that natural cognitive systems are not reactive but essentially anticipatory systems. We do not think that this is a mere coincidence. On the contrary, we claim that anticipation is a crucial–and foundational–phenomenon in natural cognition. Individual behavior is guided by anticipatory mechanisms that are used for behavioral control, perceptual processing, goal-directed behavior, and learning. And also effective social behavior relies on the anticipation of the behavior of other agents. We argue that anticipation is a key ingredient for the design of autonomous, artificial cognitive agents of the future: Only cognitive systems with anticipation mechanisms can be credible, adaptive, and successful in interaction with both the environment and other autonomous systems and humans. This is the challenge that we anticipate for the future of cognitive systems research: the passage from reactive to anticipatory cognitive embodied systems. [ABSTRACT FROM AUTHOR]

In a simulated guards-and-thieves scenario we study how the behavioral system of an autonomous agent, which consists of multiple perceptual and motor schemas endowed with anticipatory mechanisms, self-organizes for satisfying its drives. Furthermore, we study how schemas acquired for navigation can be re-used off-line, `in simulation΄, for forecasting future dangers, and planning trajectories leading to goal locations. We argue that off-line simulations permit not only to coordinate with the present, but with the future, too, and to act goal-directed. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study using two robots. In the experiment, the robots navigated through an area with or without obstacles and had the goal to shift places with each other. Four different approaches (random, reactive, planning, anticipation) were used during the experiment and the times to accomplish the task were compared. The results indicate that the ability to anticipate the behavior of the other robot can be advantageous. However, the results also clearly show that anticipatory and planned behavior are not always better than a purely reactive strategy. [ABSTRACT FROM AUTHOR]

We compare the performance of two connectionist models developed to account for some specific aspects of the decision making process in the Iterated Prisoner's Dilemma Game. Both models are based on common recurrent network architecture. The first of them uses a backward-oriented reinforcement learning algorithm for learning to play the game while the second one makes its move decisions based on generated predictions about future games, moves and payoffs. Both models involve prediction of the opponent move and of the expected payoff and have an in-built autoassociator in their architecture aimed at more efficient payoff matrix representation. The results of the simulations show that the model with explicit anticipation about game outcomes could reproduce the experimentally observed dependency of the cooperation rate on the so-called cooperation index thus showing the importance of anticipation in modeling the actual decision making process in human participants. The role of the models' building blocks and mechanisms is investigated and discussed. Comparisons with experiments with human participants are presented. [ABSTRACT FROM AUTHOR]

It seems characteristic for humans to detect structural patterns in the world to anticipate future states. Therefore, scientific and common sense cognition could be described as information processing which infers rule-like laws from patterns in data-sets. Since information processing is the domain of computers, artificial cognitive systems are generally designed as pattern discoverers. This paper questions the validity of the information processing paradigm as an explanation for human cognition and a design principle for artificial cognitive systems. Firstly, it is known from the literature that people suffer from conditions such as information overload, superstition, and mental disorders. Secondly, cognitive limitations such as a small short-term memory, the set-effect, the illusion of explanatory depth, etc. raise doubts as to whether human information processing is able to cope with the enormous complexity of an infinitely rich (amorphous) world. It is suggested that, under normal conditions, humans construct information rather than process it. The constructed information contains anticipations which need to be met. This can be hardly called information processing, since patterns from the "outside" are not used to produce action but rather to either justify anticipations or restructure the cognitive apparatus. When it fails, cognition switches to pattern processing, which, given the amorphous nature of the experiential world, is a lost cause if these patterns and inferred rules do not lead to a (partial) reorganisation of internal structures such that constructed anticipations can be met again. In this scenario, superstition and mental disorders are the result of a profound and/or random restructuring of already existing cognitive components (e.g., action sequences). This means that whenever a genuinely cognitive system is exposed to pattern processing it may start to behave superstitiously. The closer we get to autonomous self-motivated artificial cognitive systems, the bigger the danger becomes of superstitious information processing machines that "blow up" rather than behave usefully and effectively. Therefore, to avoid superstition in cognitive systems they should be designed as information constructing entities. [ABSTRACT FROM AUTHOR]

Regardless of complex, unknown, and dynamically-changing environments, living creatures can recognize situated environments and behave adaptively in real-time. However, it is impossible to prepare optimal motion trajectories with respect to every possible situations in advance. The key concept for realizing the environment cognition and motor adaptation is a context-based elicitation of constraints which are canalizing well-suited sensorimotor coordination. For this aim, in this study, we propose a polymorphic neural networks model called CTRNN+NM (CTRNN with neuromodulatory bias). The proposed model is applied to two dimensional arm-reaching movement control under various viscous force fields. The parameters of the networks are optimized using genetic algorithms. Simulation results indicate that the proposed model inherits high robustness even though it is situated in unexperienced environments. [ABSTRACT FROM AUTHOR]

In this paper we present a conceptualization and a formalization to define agents' behaviors (as exhibited in agent to agent interactions), via an extension of Petri Nets, and show how behaviors of different agents can be aligned. We explain why these agents can be considered anticipatory, and the link between Business Information Systems and anticipatory systems is elaborated. We show that alignment is a state anticipatory mechanism, where predictions about future states directly influence current behavioral decision making. This results in faster and more reliable interaction execution. Also, alignment provides a mechanism for more direct behavioral learning. We investigated three manners of alignment, individual on-the-fly alignment, pre-interaction alignment, and alignment with the intervention of a third party. This paper explains in some detail how alignment on-the-fly is realized using alignment policies. The features of the other two kinds of alignment are discussed, and future directions for research are pointed out. [ABSTRACT FROM AUTHOR]

Competitive distributed systems pose a challenge to trust modeling due to the dynamic nature of these systems (e.g. electronic auctions) and the unreliability of self-interested agents. We propose a trust model which does not assume a concrete cognitive model for other agents that an agent may interact with, but uses the discrepancy between the information provided by other agents and its own experience in order to anticipate their actions. By anticipating the behavior of other agents, an agent is able to adapt more effectively to changes in the environment for its own benefit. [ABSTRACT FROM AUTHOR]

The role of expectation in listening and composing music has drawn much attention in music cognition since about half a century ago. In this paper, we provide a first attempt to model some aspects of musical expectation specifically pertained to short-time and working memories, in an anticipatory framework. In our proposition anticipation is the mental realization of possible predicted actions and their effect on the perception of the world at an instant in time. We demonstrate the model in applications to automatic improvisation and style imitation. The proposed model, based on cognitive foundations of musical expectation, is an active model using reinforcement learning techniques with multiple agents that learn competitively and in collaboration. We show that compared to similar models, this anticipatory framework needs little training data and demonstrates complex musical behavior such as long-term planning and formal shapes as a result of the anticipatory architecture. We provide sample results and discuss further research. [ABSTRACT FROM AUTHOR]

This paper presents the first basic principles, implementation and experimental results of what could be regarded as a new approach to reinforcement learning, where agents—physical robots interacting with objects and other agents in the real world—can learn to anticipate rewards using their sensory inputs. Our approach does not need discretization, notion of events, or classification, and instead of learning rewards for the different possible actions of an agent in all the situations, we propose to make agents learn only the main situations worth avoiding and reaching. However, the main focus of our work is not reinforcement learning as such, but modeling cognitive development on a small autonomous robot interacting with an "adult" caretaker, typically a human, in the real world; the control architecture follows a Perception-Action approach incorporating a basic homeostatic principle. This interaction occurs in very close proximity, uses very coarse and limited sensory-motor capabilities, and affects the "well-being" and affective state of the robot. The type of anticipatory behavior we are concerned with in this context relates to both sensory and reward anticipation. We have applied and tested our model on a real robot. [ABSTRACT FROM AUTHOR]

We introduce information-theoretic tools that can be used in an autonomous agent for constructing an internal predictive model based on event anticipation. This model relies on two different kinds of predictive relationships: time-delay relationships, where two events are related by a nearly constant time-delay between their occurrences; and contingency relationships, where proximity in time is the main property. We propose an anticipation architecture based on these tools that allows the construction of a relevant internal model of the environment through experience. Its design takes into account the problem of handling different time scales. We illustrate the effectiveness of the tools proposed with preliminary results about their ability to identify relevant relationships in different conditions. We describe how these principles can be embedded in a more complex architecture that allows action-decision making according to reward expectation, and handling of more complex relationships. We conclude by discussing issues that were not addressed yet and some axis for future investigations. [ABSTRACT FROM AUTHOR]

This chapter outlines an approach to building robots with anticipatory behavior based on analogies with past episodes. Anticipatory mechanisms are used to make predictions about the environment and to control selective attention and top-down perception. An integrated architecture is presented that perceives the environment, reasons about it, makes predictions and acts physically in this environment. The architecture is implemented in an AIBO robot. It successfully finds an object in a house-like environment. The AMBR model of analogy-making is used as a basis, but it is extended with new mechanisms for anticipation related to analogical transfer, for top down perception and selective attention. The bottom up visual processing is performed by the IKAROS system for brain modeling. The chapter describes the first experiments performed with the AIBO robot and demonstrates the usefulness of the analogy-based anticipation approach. [ABSTRACT FROM AUTHOR]

Visual forward models predict future visual data from the previous visual sensory state and a motor command. The adaptive acquisition of visual forward models in robotic applications is plagued by the high dimensionality of visual data which is not handled well by most machine learning and neural network algorithms. Moreover, the forward model has to learn which parts of the visual output are really predictable and which are not because they lack any corresponding part in the visual input. In the present study, a learning algorithm is proposed which solves both problems. It relies on predicting the mapping between pixel positions in the visual input and output instead of directly forecasting visual data. The mapping is learned by matching corresponding regions in the visual input and output while exploring different visual surroundings. Unpredictable regions are detected by the lack of any clear correspondence. The proposed algorithm is applied successfully to a robot camera head under additional distortion of the camera images by a retinal mapping. Two future applications of the final visual forward model are proposed, saccade learning and a task from the domain of eye-hand coordination. [ABSTRACT FROM AUTHOR]

This paper presents a set of techniques that allow generating a class of testbeds that can be used to test recurrent neural networks' capabilities of integrating information in time. In particular, the testbeds allow evaluating the capability of such models, and possibly other architectures and algorithms, of (a) categorizing different time series, (b) anticipating future signal levels on the basis of past ones, and (c) functioning robustly with respect to noise and other systematic random variations of the temporal and spatial properties of the input time series. The paper also presents a number of analysis tools that can be used to understand the functioning and organization of the dynamical internal representations that recurrent neural networks develop to acquire the aforementioned capabilities, including periodicity, repetitions, spikes, and levels and rates of change of input signals. The utility of the proposed testbeds is illustrated by testing and studying the capacity of Elman neural networks to predict and categorize different signals in two exemplary tasks. [ABSTRACT FROM AUTHOR]

Some visual search tasks require the memorization of the location of stimuli that have been previously focused. Considerations about the eye movements raise the question of how we are able to maintain a coherent memory, despite the frequent drastic changes in the perception. In this article, we present a computational model that is able to anticipate the consequences of eye movements on visual perception in order to update a spatial working memory. [ABSTRACT FROM AUTHOR]

This paper describes the integration of several cognitively inspired anticipation and anticipatory learning mechanisms in an autonomous agent architecture, the Learning Intelligent Distribution Agent (LIDA) system. We provide computational mechanisms and experimental simulations for variants of payoff, state, and sensorial anticipatory mechanisms. The payoff anticipatory mechanism in LIDA is implicitly realized by the action selection dynamics of LIDA's decision making component, and is enhanced by importance and discrimination factors. A description of a non-routine problem solving algorithm is presented as a form of state anticipatory mechanism. A technique for action driven sensational and attentional biasing similar to a preafferent signal and preparatory attention is offered as a viable sensorial anticipatory mechanism. We also present an automatization mechanism coupled with an associated deautomatization procedure, and an instructionalist based procedural learning algorithm as forms of implicit and explicit anticipatory learning mechanisms. [ABSTRACT FROM AUTHOR]

We present a theoretical analysis of schema-based design (SBD), a methodology for designing autonomous agent architectures. We also provide an overview of the AKIRA Schema Language (AKSL), which permits to design schema-based architectures for anticipatory behavior experiments and simulations. Several simulations using AKSL are reviewed, highlighting the relations between pragmatic and epistemic aspects of behavior. Anticipation is crucial in realizing several functionalities with AKSL, such as selecting actions, orienting attention, categorizing and grounding declarative knowledge. [ABSTRACT FROM AUTHOR]

The paper proposes the framework for an animat control system (the Animat Brain) that is based on the Petr K. Anokhin's theory of functional systems. We propose the animat control system that consists of a set of functional systems (FSs) and enables predictive and purposeful behavior. Each FS consists of two neural networks: the actor and the predictor. The actors are intended to form chains of actions and the predictors are intended to make prognoses of future events. There are primary and secondary repertoires of behavior: the primary repertoire is formed by evolution; the secondary repertoire is formed by means of learning. This paper describes both principles of the Animat Brain operation and the particular model of predictive behavior in a cellular landmark environment. [ABSTRACT FROM AUTHOR]

We review some of the main theories about how language emerged. We suggest that including the study of the emergence of artificial languages, in simulation settings, allows us to ask a more general question, namely, what are the minimal initial conditions for the emergence of language? This is a very important question from a technological viewpoint, because it is very closely tied to questions of intelligence and autonomy. We identify anticipation as being a key underlying computational principle in the emergence of language. We suggest that this is in fact present implicitly in many of the theories in contention today. Focused simulations that address precise questions are necessary to isolate the roles of the minimal initial conditions for the emergence of language. [ABSTRACT FROM AUTHOR]

How can goals be represented in natural and artificial systems? How can they be learned? How can they trigger actions? This paper describes, analyses and compares two of the most influential models of goal-oriented behavior: the ideomotor principle (IMP), which was introduced in the psychological literature, and the "test, operate, test, exit" model (TOTE), proposed in the field of cybernetics. This analysis indicates that the IMP and the TOTE highlight complementary aspects of goal-orientedness. In order to illustrate this point, the paper reviews three computational architectures that implement various aspects of the IMP and the TOTE, discusses their main peculiarities and limitations, and suggests how some of their features can be translated into specific mechanisms in order to implement them in artificial intelligent systems. [ABSTRACT FROM AUTHOR]

Animals anticipate the future in a variety of ways. For instance: (a) they make motor actions that are timed to a reference stimulus and motor actions that anticipate future movement dynamics; (b) they learn to make choices that will maximize reward they receive in the future; and (c) they form memories of behavioral episodes such that the animal's future actions can be predicted by current neural activity associated with those memories. Although these effects are clearly observable at the behavioral level, research into the mechanisms of such anticipatory learning are still largely in the early stages. This review, intended for those who have a computational background and are less familiar with neuroscience, addresses neural mechanisms found in the mammalian cerebellum, basal ganglia, and the hippocampus that give rise to such adaptive anticipatory behavior. [ABSTRACT FROM AUTHOR]

Research on anticipatory behavior in adaptive learning systems continues to gain more recognition and appreciation in various research disciplines. This book provides an overarching view on anticipatory mechanisms in cognition, learning, and behavior. It connects the knowledge from cognitive psychology, neuroscience, and linguistics with that of artificial intelligence, machine learning, cognitive robotics, and others. This introduction offers an overview over the contributions in this volume highlighting their interconnections and interrelations from an anticipatory behavior perspective. We first clarify the main foci of anticipatory behavior research. Next, we present a taxonomy of how anticipatory mechanisms may be beneficially applied in cognitive systems. With relation to the taxonomy, we then give an overview over the book contributions. The first chapters provide surveys on currently known anticipatory brain mechanisms, anticipatory mechanisms in increasingly complex natural languages, and an intriguing challenge for artificial cognitive systems. Next, conceptualizations of anticipatory processes inspired by cognitive mechanisms are provided. The conceptualizations lead to individual, predictive challenges in vision and processing of event correlations over time. Next, anticipatory mechanisms in individual decision making and behavioral execution are studied. Finally, the book offers systems and conceptualizations of anticipatory processes related to social interaction. [ABSTRACT FROM AUTHOR]

Empirical evidence indicates that anticipatory representations grounded in the sensorimotor neural apparatus are crucially involved in several low and high level cognitive functions, including attention, motor control, planning, and goal-oriented behavior. A unitary theoretical framework is emerging that emphasizes how simulative capabilities enable social abilities, too, including joint attention, imitation, perspective taking and communication. We argue that anticipation will be a key element for bootstrapping high level cognitive functions in cognitive robotics, too. We thus propose the challenge of understanding how anticipatory representations, that serve for coordinating with the future and not only with the present, develop in situated agents. [ABSTRACT FROM AUTHOR]

We analyze the role of motivations in living organisms, and the nature of their influences on behavior with the aim to propose a design methodology for schema-based agent architectures. We propose that motivations have a modulatory influence on behavior, and in our design methodology they regulate the allocation of resources to the sensorimotor system and schemas. We describe an agent architecture incorporating this principle and we highlight its performance in a simulative study. [ABSTRACT FROM AUTHOR]