Introduction

With the advance of the Internet, mankind will become more networked and will have to live together with a number of intelligent autonomous agents. WEB 2.0 is the symbolic term to refer to this trend. The agents would have a variety of purposes, but their ultimate goal would be to ensure a better life for humans. However, useful models for designing such agents have not been provided in the research disciplines, such as Cognitive Sciences and Information Technology. This is mainly because traditional linear and reductionistic approaches would not be appropriate for modeling human beings, or brains, which exhibit a multi-layered structure with nonlinear interconnections (Prigogine, 1997).

Nonlinearity means two important things that affect development of individual brain–society system:

• Dissipative system: A fluctuation of the system caused by an environmental change would trigger creation of a new order or catastrophe, and
• Sensitive Dependence on Initial Condition (SEDIC): A small variation in the initial condition, during one’s infant period, would develop exponentially as one grows up.

MSA: Maximum Satisfaction Architecture

Maximum Satisfaction Architecture (MSA) consists of three parts: 1) happiness goals, i.e., basic living purposes of human beings, 2) human brain, and 3) society, with the aim of providing a basis for designing intelligent autonomous agents that contribute to realizing better living.

• Happiness goals. MSA assumes that the human brain pursues one of the seventeen happiness goals defined by Morris(2006) at every moment, and switches when appropriate by evaluating the current circumstances.
• Society layers. Each of the happiness goals is associated with one or multiple layers of Society: Individual, Family and Community, and Administration and Enterprise. These layers have evolved from the history of human beings. Each layer is associated with its own value reflecting historical development, and thus different sets of happiness goals are relevant.
• Brain layers. The knowledge necessary to achieve the happiness goals is partly acquired and partly inherited. At the level of Conscious layer, knowledge such as formal laws and social mechanisms necessary to deal with administration and enterprise, and formal social norms and common sense to deal with “family and community” and “individual” is acquired. In contrast, knowledge such as basic functions for using language and primitive decision characteristics is inherited. Similarly, at the level of Autonomous-automatic behavior control layer, knowledge such as individual experience and habit is acquired to deal with “family and community” and “individual.” However, as opposed to the inherited knowledge at the Conscious layer, all basic functions that are reproducible by development and bodily experience are inherited in the Autonomous-automatic behavior control layer.

The pieces of knowledge at each layer in the brain are nonlinearly interconnected through individual experience. This implies that individuals that pursue the same goal might have different patterns of activated networks because of SEDIC, and thus the processes to achieve the goal might be different.

An intelligent autonomous agent must be sensitive to the individual differences in the processes to achieve a goal and provide sophisticated support for individuals to achieve that goal.

Conditions to make people feel satisfaction

The amount of satisfaction feeling is influenced by the factors that characterize the shape of trajectory of behavioral outcome. There are six critical factors to make people feel satisfaction.

1. Change: Perceptual functions work by sensing dynamic changes. Therefore responses while the system is stable are limited. A condition for feeling satisfactory feeling is "change."
2. Succession of good results: Successive happiness tends to create memory traces for the best experience and the final outcome of the overall estimation of the events that have lead to successive good results.
3. Direction of absolute outcome (denoted as 2 in the Figure): A change to good direction at the end of a series of events tends to create a memory trace of having satisfactory feeling. The degree of the strength of the memory trace would be proportional to the degree of the change towards good direction.
4. Amplitude of success (denoted as 1 in the Figure): The greater the difference between the highest event and the lowest event in terms of the degree of the strength of satisfactory feeling, the stronger the strength of memory trace for the entire events including the highest and the lowest. One would create satisfactory feeling as the result of overcoming the lowest evaluated situation and tends to memorize it.
5. Absolute amount of outcome (denoted as 3 in the Figure) and Direction of absolute outcome (denoted as 2 in the Figure): When the absolute outcome is acceptable and the contents in working memory at the time of final event are good, they jointly affect the result of estimation of entire events.
6. Bad results would not be memorized: When an event occurs that results in bad results, one would strongly react to it when the degree of badness exceeds a certain threshold value. This event would create a memory trace for must-avoid-event. However, memory traces for the events that would be exerted while recovering from the bad situation tend to be weak because conscious processes would work in their full performance.

Working of MSA-based intelligent autonomous agents:

The main functions of an MSA-based intelligent autonomous agent that aids the achievement of a happiness goal would include:

• Promoting a state in the autonomous-automatic behavior control layer to a state in the conscious layer,
• Supporting decision making, and
• Activating interaction between the conscious layer and the unconscious autonomous-automatic behavior control layer.

References

Morris, D. (2006). The nature of happiness. 48 Catherine Place, London SW1E 6HL: Little Books Ltd.

Prigogine, I. (1997). The end of certainty. Free Press.

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Summary

At present, there is no research method for viewing the brain from a broad perspective. We suggest that theories of complex systems like fluid are useful. We therefore propose Brain Information Hydrodynamics (BIH) as a theory that should serve as a basis for constructing a Model Brain, which is traditionally conceived as an electronically based neuronal network and / or chemically based hormone field. In BIH, the influx of information from the environment is filtered at the entrance of the brain to reduce the amount of information to a tractable number of chunks. The influx flows along the terrain, which was originally shaped by genes and then transformed through experience. Immediate behavior is generated when the influx reaches the cerebellum directly. Deliberate behavior, the outcome of the cerebrum, is generated when the influx is trapped midway to the cerebellum where a number of vortices are created to transform the values of attributes of the information that the influx is conveyed successively to the ones finally exerted. The real-time constraint of behavior is satisfied by creating emotional vortices that force the flow to reach the cerebellum in a timely manner.

BIH deals with information flow in the brain and its characteristics in the time dimension. Biological activity can be viewed as the results of information flow in the brain and it shows the characteristics of complex systems and dissipative structure. In other words, it is best characterized by hydrodynamics at the microscopic phenomenological level and by thermodynamics at the macroscopic collective level.

Time axis is central to information flow

Time is the fourth dimension of our four-dimensional physical universe. However, unlike the X- Y- Z- dimensions, it is not symmetric, in other words, it is not reversible. The order of our universe is being shaped as the interactions between life and the surrounding environment and develops along the one-directional time dimension. The characteristic times of brain information processing ensure sustainability of those interactions.

The functioning brain is the result of the working of a huge network of 20 billion nerve cells and synapses. It basically converts input signals from the environment to information that is necessary for acting in real time. However, the phenomena that the flow of information in the brain causes are extraordinarily complex. We suggest that this is analogous to the complexity of the phenomena exhibited by a flow of fluid and that it is useful to apply the construct of the theory of hydrodynamics metaphorically to the phenomena of information flow in the brain.

Cerebrum formation process

In the very early days, the organisms first created cerebellum-like feed-forward networks. They were most suitable for generating prompt responses to the occurrence of libido, which is the free creative energy an individual has to apply to personal development. Those networks enabled the organisms to perform the required sequence of actions very smoothly: collecting information from the external environment, taking actions for satisfying the occurring libido, achieving it, and finally, returning to the resting state.

After developing the cerebellum-like feed-forward networks, organisms then developed the cerebrum. As opposed to cerebellum, the cerebrum is equipped with feedback networks for processing information. These networks enabled the organisms to perform complicated information processing that was impossible for cerebellum-like feed-forward networks.

How have the feedback-networks developed from the feed-forward networks? Here is our answer. When libido occurs, information from the external environment is gathered via sensory organs, eyes for visual information, nose for olfactory information, and ears for auditory information. The set of information originating from the variety of sensors with different modalities constitutes a set of information flows in the brain network. They flow simultaneously and quasi-independently, and are ultimately transformed into the information for generating external actions.

However, the pattern of the flows is very complex because individual flows are not synchronous in time but the set of flows must converge at the time when an action associated with the input is taken. The timing of action is strictly determined by real time constraints. Some flows may have spare time and have to wait until the other flows are ready to be integrated, or synchronized. While waiting, the flows of information may develop an order that is analogous to vortices in the stream of river. In the brain network, informational vortices may develop, drift, disappear, fission, and merge. A vortex can interact with the other vortices. These vortices can be conceived as manifestation of some functions that work as part of feedback control. See Figs. 1 and 2 for details.

Figure 1. Interaction between brain and environment based on feed-forward control.

Figure 2. Formation of cerebrum.

Information flows in the brain

The brain consists of the three following non-linearly connected layers and functions by activating part of the structure.

• C-layer: Conscious state layer
• A²BC: Autonomous-automatic behavior control layer
• B-layer: Bodily state layer

Information flows in each layer with its specific purpose (Fig. 3). In the C-layer, information is for predicting the time course of events and for coordinating relationships between the self and others. In the A²BC-layer, information is for autonomously and automatically controlling a variety of parts of the body. In the B-layer, information is for regulating the bodily state.

Vortices emerge in a river when the amount of flow exceeds some threshold. Similarly, when the amount of information flow in the brain exceeds a certain threshold, informational vortices emerge in the network. These vortices correspond to some conscious states.

In BIH, emotions are regarded as the phenomena by which the information flows in the three layers are interrupted in order to take timely actions, in other words, the real time constraints intervene in the information flows that may not converge and synchronize at the time an action must be taken. The vortices collapse immediately, i.e., conscious thinking terminates in favor of taking timely action.

In summary, consciousness and emotion function jointly for determining communication behavior. This topic will be further discussed in the section Dynamics of consciousness-emotion interaction: an explanation by NDHB-Model/RT.

Figure 3. Information flows in the brain.

Biorhythm of information flow

Information flow in the brain has a one-day cycle. While sleeping, the amount of the flow stays at a minimum level. In the daytime, the flow increases to the maximum level while working intensively. However, the actual amount of flow is determined by the relationships between the state of the external environment and the desire of the self. Metaphorically, the one-day cycle of information flow is similar to the daily changes of the ebb and flow in a narrow strait where the emergence of vortices depends on the amount of the one-directional tidal stream.

Role of language

The vortices emerge spontaneously when conditions are satisfied. However, as the skill of using language developed and matured, and began to be stably inherited among generations, it began to work as triggers to make vortices emerge, like pegs cause turbulent flows to make vortices emerge.

Multiple personality disorder

When making decisions, a number of candidate actions are evaluated for their suitability in the current situation. However, the actions that are actually taken are largely determined by the evaluation performed by the experience-based reward system located at the junction of the cerebrum and the cerebellum. This evaluation process is unconscious.

In the human brain, there coexist multiple personalities by nature. In the cerebrum, there are a number of small-scale networks that serve as elements for defining personality. The combination of the partial elements, which is the result of information flow in the cerebrum, is determined by the reward system, and therefore there is the possibility of emergence of one personality for some situation and another for a different situation. Which personality emerges depends solely on the external information that is fed to the brain, the contingency of selection of the route of information flow in the cerebrum, and the nature of the experience-based reward system.

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Summary

The recent consensus is that the range of informational inheritance by genes is limited to physical functions and infantile behavior. Human beings need to acquire basic behavioral skills and communicational skills through experience of behaving in the environment. We propose Structured Meme Theory that explains acquisition and development of these skills. Structured Meme Theory consists of action-level, behavior-level and culture-level memes. These are interconnected non-linearly and reflect the level of complexity of brain functions that map information in the environment onto internal representations. The mechanism with which the three levels of memes and genes inherit information is analogous to an information system. Genes serve as firmware that mimics behavior-level activities. Action-level memes serve as the operating system that defines general patterns of spatial-temporal behavioral functions. Behavior-level memes serve as middleware that extends the general patterns to concrete patterns. Culture-level memes serve as application tools that extend the concrete patterns to the ones that work in a number of groups of people.

Structured Meme

A meme is an entity that represents the information associated with the object that the brain can recognize. The original term “meme” Richard Dawkins coined in the 1970s was conceptual and was not defined clearly. However, the meme, or the structured meme, in SMT proposed in this paper is defined clearly within the framework of a non-linear, multilayered information structure that is similar to the structure of living organisms.

A meme is defined as follows. Each object is defined as a set of elements that belong to each layer in a nonlinearly connected multi-layered structure. Those elements that are recognizable as proper entities, such as shape, movement, and quality, are able to exist as memes – latent memes. These latent memes change to manifest memes when they are fixated as part of an object or memorized by the other persons as information objects through the experience that the self takes part in.

As such, memes exist in the brain not only as entities that correspond to real objects that exist in the environment but also as information objects that are included in the layers the elements of the objects belong to. For human beings, the latter has been constructed by mapping environmental information onto the networks in the brain, which has established the relationships between human beings and their surrounding environment. This explains the emergence of cultural differences among living groups.

The structured meme consists of three non-linear layers.

• Action-level memes represent bodily actions.
• Behavior-level memes represent behaviors in the environment.
• Culture-level memes represent culture.

Memes as a whole are a collection of information objects that reside in each layer. Each person will develop his/her own relationships among objects.

Figure 1 depicts the structure of inheritance of information in which genes, memes, and language participate.

Figure 1. The structure of meme.

Memes propagate by means of resonance

Memes propagate from person to person when the receiver estimates that the degree of reality of the meme perceived by him/her reaches a certain level. The process of feeling reality can be conceived as the process of resonance that occurs in the brain in response to the input of memes from a sender. When the meme in question resonates with some patterns associated with valued experiences endorsed by the reward system, the meme is accepted by the receiver. The entire meme structure in human society is a networked field defined by an individual’s connections. Each person’s brain forms a proper reality field, and it builds up to the entire reality field. Memes propagate in the thus constructed reality field by means of resonance.

Figure 2 illustrates how memes propagate in the reality field. The process of propagation is facilitated by symbolization. A symbolized meme enables people to think on abstract levels.

Figure 2. Propagation of meme.

Characteristics of meme propagation

A meme is defined as a matrix-like construct that consists of multiple layers and a number of elements. The feeling of reality that an individual experiences is formed by integrating responses generated by the acceptor elements whose structure is defined similarly to that of the structured meme. However, the response sensitivity of the individual’s acceptor elements is shaped by experience, and thus it exhibits individual differences depending on the individual’s experience.

While a meme is propagating in the network of individuals, the differences in reality responses by individuals also propagate. This implies that the meme may be altered in the propagation process.

Figure 3 depicts the cultural evolution of a meme. It also demonstrates that some amount of fluctuation results in meme quantity and quality because the propagation cannot completely reflect the complexity of the environment.

Figure 3. Evolution of meme.

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Summary

Traditional cognitive sciences have not treated human behavior as the result of intense interaction between consciousness and emotion. Rather, these two functions have been studied separately. However, in the existing internet era, it is urgently necessary to develop unified theories that can deal with the dynamics of consciousness-emotion interaction in order to design appropriate information systems. This paper explains the interaction based on an architecture model we have been developing as a candidate for such unified theories, the Nonlinear Dynamic Human Behavior Model with Real-Time Constraints, NDHB-Model/RT, presented at CogSci2007 [3] and CogSci2008 [1,2]. NDHB-Model/RT represents consciousness as one-dimensional linear operations (language) and emotion as a hydrodynamic flow of information in multi-dimensional parallel operations in the neural networks. NDHB-Model/RT also has autonomous memory systems that mediate between consciousness and emotion to display their dynamic interactions. Model Human Processor with real time constraints, MHP/RT, is proposed as the simulation model on NDHB-Model/RT.

Features of behavioral decisions

The brain consists of the following three non-linearly connected layers. Behavioral decisions are made by integrating the results of operations of these three layers.

• C layer: Conscious state layer
• A²BC layer: Autonomous-automatic behavior control layer
• B layer: Bodily state layer

The bodily state layer prioritizes the 17 behavioral goals presented in Fig. B. The other two layers interact with each other in order to derive the next behavior that should satisfy the highest prioritized goal. In normal situations in our daily life, temporal changes in the environment impose the strongest constraint on the decision of the next behavior, and thus the A²BC layer plays a more dominant role than the C layer in organizing behavior.

The next behavior is determined by extracting objects from the ever-changing environment and attaching values to them according to the degree of the strength of the resonance with what is stored in the autonomic memory system. This is followed by deliberate judgement by using the knowledge associated with the highly valued objects. The former is controlled by the processes in the A²BC layer; the latter, by the processes in the C layer.

Interaction between consciousness and emotion

The processes in the A²BC layer and those in the C layer are not independent. Rather, they interact with each other very intensely in some cases but very weakly in other cases. We investigate this issue in more detail below.

Onset of consciousness

With the onset of arousal, the sensory organs begin to collect environmental information. This information flows into the brain, and the information flow volume grows rapidly. As the information flow circulates in the neural networks, the center of the flow gradually emerges. It corresponds to the location where the successive firings of the neural networks concentrate. At this time, the center of information flow induces activities in the C layer via the cross-links in the neural networks.

Conscious activities

Figure 1 depicts the state of the brain when consciousness starts working. The location of consciousness is indicated as a dot in the C layer. In many cases, the working of consciousness includes such cognitive activities as comprehension of self-orientation and an individual’s circumstances. When decision making is needed for the current situation, the location of consciousness could move. The direction of movement is determined by the information needs at that time. It could move either in the direction in which the initial information is deepened (left in the figure) or to the direction in which the initial information is widened (right in the figure). The density of information would change depending on how far the center of consciousness moves. However, the location of the consciousness would not move when carrying out a routine task.

Figure 1. Onset of consciousness and emergency of emotion.

Emergence of emotion

After the onset of consciousness, a new thread of information coming into the brain via the sensory organs triggers successive firing within the neural networks. This causes a new information flow in the brain that reflects past experience that resonates with the input information. If there is a discrepancy between the new information flow (the dotted line in the figure) and the existing information flow (the solid line in the figure), emotion emerges. Emotion works to reduce the amount of discrepancy.

Determination of next behavior

When the A²BC layer works continuously within its capacity, consciousness does not interfere with the working of the A²BC layer but monitors the individual’s behavior, prepares for the next behavior, and/or ponders issues that come to mind. However, if the A²BC layer has difficulty in determining the next behavior, the C layer takes over and determines it. The following depicts the flow of the processes that would happen (see Fig. 2).

1. Consciousness determines the next behavior by considering the current state of emotion and the self-recognition.
2. Consciousness tunes the orientation of the sensory organs in preparation for initiating the next behavior just determined.
3. Consciousness commands initiating the next behavior.
4. The behavior results in changes in the information flow.
5. The direction of emotion changes.
6. The new state of emotion affects the process of determining the next action.

Figure 2. Determination of next behavior.

Synchronization between the C layer and the A2BC layer: Model Human Processor with Real Time Constraints

The most important assumption of the NDHB-Model/RT is that the human brain works under real-time constraints governed by the environment, largely uncontrollable from the brain. We assume that the C layer and the A²BC layer operate together in order to determine the next behavior. However, as described above, the interaction between them could be weak or strong, depending on the situation. There thus needs to be a synchronization mechanism for them to work together appropriately.

We suggest that the visual-frame reconstruction process in the C layer should be used for establishing synchronization between the C layer and the A²BC layer. As depicted in Fig. 3, the C layer predicts the representation of the visual frame that should appear in the future and uses it for synchronization. When the A²BC layer mainly controls the behavior, the visual-frame rate would be around 10 frames per second, and the C layer would monitor the self-behavior by occasionally matching the expected visual frame and the real visual frame in the A²BC layer. In contrast, when the C layer mainly controls the behavior, the rate would become lower and vary depending on the interest of consciousness. For the former situation, the visual-frame density is high but the information density is low; for the latter situation, the visual-frame density is low but the information density is high. This explanation is consistent with the well-known Newell’s Time Scale of Human Action [4].

Figure 3. Model Human Processor with Real Time Constraints, MHP/RT.

References

1. Kitajima, M., Toyota, M., & Shimada, H. (2008). Model Brain: Brain Information Hydrodynamics. Proceedings of the 30th Annual Meeting of the Cognitive Science Society, 1453.
2. Toyota, M., Kitajima, M., & Shimada, H. (2008). Structured Meme Theory: How Is Informational Inheritance Maintained? Proceedings of the 30th Annual Meeting of the Cognitive Science Society, 2288.
3. Kitajima, M., Shimada, H., & Toyota, M. (2007). MSA:Maximum Satisfaction Architecture: A Basis for Designing Intelligent Autonomous Agents on WEB 2.0. Proceedings of the 29th Annual Meeting of the Cognitive Science Society, 1790.
4. Newell, A. (1994). Unified Theories of Cognition. Harvard University Press.

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Summary

Traditional interactive systems transform input to the systems from the environment to output in the environment by using a set of rules. However, these systems are not intelligent enough to respond to an ever-changing environment including users. There are thus cases where inputs to a system may drift too far to be treated by the set of rules, and the system might respond inappropriately. This paper proposes a new perspective on interactive system design. The key idea is to treat interactive systems as autonomous systems that interact with users that are other autonomous systems, and designing interactive systems implies designing autonomous system interactions that establish natural cooperation among them. At CogSci2007[3] and CogSci2008[1,2], we proposed an architecture model called the Nonlinear Dynamic Human Behavior Model with Real-Time Constraints, NDHB-Model/RT. We demonstrate in this paper that this architecture model provides a basis for Autonomous System Interaction Design.

Society of Autonomous Systems

Human beings interact with an environment that includes interactive systems. This section starts by describing a society of systems having the property of linearity or autonomy, followed by the needs that those systems must satisfy and the proposal of autonomous systems interaction that should meet the requirements.

Linear Systems

Behaving objects in the environment are defined in four-dimensional space-time coordinates. A human being viewed as a linear system acquires information of behaving objects via its sensory organs as two-dimensional data. The four-dimensional data are reduced to two-dimensional data in this process. The input data are then used for representing their characteristics by means of static linear functions. When an objective of behavior is given, the linear system will behave by deriving static solutions by using the linear functions that best match the current situation.

Figure 1 illustrates a society of linear systems managing various situations by tuning the relationships among the constituent systems. However, there are situations where the current organization of the systems causes a large amount of stress in spite of efforts made to resolve the situations and they cannot behave properly. In these situations, the systems have to change themselves. However, the change may or may not produce good results. In the worst cases, the change may cause a rapid increase of stress and crash the system.

Autonomous Systems

Human beings viewed as autonomous systems represent behaving objects in the four-dimensional space-time environment via sensory organs. For example, the sense of taste is represented by six-dimensional data and the sense of sight is represented by four-dimensional data. The input data are processed mainly by the A²BC-layer (Autonomous-Automatic Behavior Control layer) and the B-layer (Bodily state layer), and optionally by the C-layer (Conscious state layer) in the brain, and used to define functions that work in SMT [2] and MSA [3] with the real-time constraints defined by BIH [1]. The functions accumulate personal four-dimensional experience continuously. When an objective of behavior is given, the autonomous system will behave by deriving effective regions so that the self will behave properly by using the functions.

When an autonomous system communicates with another one, it uses the effective region at each moment. This assures less stressful communication among autonomous systems than among linear systems (Fig. 1).

Figure 1. Society of linear systems and society of autonomous systems.

Needs that a Society of Information Systems Must Meet

This paper suggests that autonomy of systems is necessary for establishing an effective society of interactive systems because the current society has become rich and has to satisfy each individual’s diverse needs. The needs of the society include the following.

• Need for efficiency, effectivity and low price. This is satisfied by developing high-performance systems with integrated functionalities. However, it is important to match the performance of the systems with the performance of brain functioning by considering the characteristics of human beings based on NDHB-Model/RT.
• Need for ease of use. This depends on an individual’s knowledge and its use. This need has priority over the need for efficiency, effectivity and low price. The use of knowledge is mainly defined by SMT.
• Need for satisfaction. This need depends on an individual’s experience. This is satisfied by developing an autonomous systems society that can deal with diversity in the evaluation criteria and their temporal changes.

Outline of Autonomous System Interaction (ASI)

The current social system is built on the traditional interaction model that assumes linearity of the society. As described above, there are serious limitations in linear systems when trying to satisfy diverse individuals’ needs. In the following, this paper outlines autonomous systems interaction that should satisfy the above-mentioned needs for a society of information systems.

An autonomous system monitors its environment continuously and initiates communication with the other autonomous systems when needed. There are three purposes of ASI.

• It helps enhance the autonomy of human beings.
• It adds autonomy to devices.
• It helps maintain harmony of the entire society.

In order to achieve these purposes, autonomous system interaction includes the following characteristics:

• request for information,
• support for help, or
• guide for action.

Initiation of communication includes such activities as 1) direct the other party’s attention to the initiator and 2) synchronize activities among the participants. An autonomous system takes the initiative in order to maintain communication. There are two types of information in ASI. One is static information that is used for the analysis of objectives and evaluation. The other is dynamic information that is used for organizing future courses of behavior. The static information is acquired either by

• monitoring without notice, which means that the system does not notice that it is monitored, or
• monitoring with notice, in which the monitored system knows that it is being monitored.

The dynamic information is used for emergency control, supportive control, or full control.

In summary, consciousness and emotion function jointly for determining communication behavior. Figure 2 depicts an example of a society that is designed by means of autonomous system interaction (ASI).

Figure 2. An example of society composed of autonomous systems interaction (ASI).

Conclusion

This paper proposed a concept of autonomous system interaction design that is most suitable for constructing a society of interactive systems including human beings. All the constituent systems are modeled and designed as autonomous systems, and thus interactions among them are symmetric. Coordination of systems in pursuit of satisfying the current objectives is achieved through participation of all the systems: each system behaves autonomously for achieving the objectives. Autonomous systems are designed by assuming that human beings behave according to the NDHB-Model/RT.

A society of systems that consists of personal decision support systems, operation support systems, mobile communication support systems, and public support systems would be a typical organization of autonomous system interaction as depicted in Fig. 2. Each autonomous system has its characteristic regions in the spatial-temporal and information dimensions, and it decides what to do next by using a decision-making algorithm that is specific to the system. When deciding, the system monitors the other systems that are relevant to the current decision making and requests information when necessary in order to make better decisions by considering the other systems’ behavior. The systems iterate this fundamental coordination process to achieve a stable and effective solution for the current objectives.

References

1. Kitajima, M., Toyota, M., & Shimada, H. (2008). Model Brain: Brain Information Hydrodynamics. Proceedings of the 30th Annual Meeting of the Cognitive Science Society, 1453.
2. Toyota, M., Kitajima, M., & Shimada, H. (2008). Structured Meme Theory: How Is Informational Inheritance Maintained? Proceedings of the 30th Annual Meeting of the Cognitive Science Society, 2288.
3. Kitajima, M., Shimada, H., & Toyota, M. (2007). MSA:Maximum Satisfaction Architecture: A Basis for Designing Intelligent Autonomous Agents on WEB 2.0. Proceedings of the 29th Annual Meeting of the Cognitive Science Society, 1790.
   
   

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