Section I: Application areas:

Relevance: This example technology illustrates that emerging new technologies are changing the nature of processes that occur in the Earth system. These technologies are coupled with a social awareness of the importance of group processes that concern the whole Earth.

If there is a collective unconsciousness, as posited by scholar Karl Jung and others, then the mechanisms that support this phenomenon are surely being changed by the Internet and by global communications. The example technology makes the provision of a "collective unconsciousness" to clusters of Earth orbit satellites and to groups of autonomus space craft. These may effect mechanisms, if they exist, that provide a common substrate to human thinking.

Awareness is a property that occurs under conditions that modern science can only now conjecture about. Some scholars have conjectured that these conditions must include a type of cross scale coupling of intentional processes to metabolic and other processes involving exchanges of energy. We agree that this conjecture is interesting. We see that awareness is not a simple process, involving only one level of organization. Awareness involves observable interactions, with well defined exchanges of energy, as well as un-observed causes. These "causes" are conjectured to be related to the substracture of memory or related to the process constraints of the environment. Within this paradigm, a level of organization is defined to be all those things that interact with minimal, or no, un-observed causes. For example, we would be able to see things only within one level of organization. We do not see the quantum mechanical flux that arrives from the world into our optic nerve. Also within this paradigm, awareness has multiple levels of organization that contribute to it's reality, and only one of these levels can be observed.

The example technology sets up the conditions under which the substructural, memory, level and the ultrastructural, categorical constrant, level entangle to produce an artificial awareness for an system of autonomous, or semi-autonomous, spacecraft. This "tri-level" architecture of artificial knowledge processing in networks is set up in a correspondance with what we feel is feasibly consistant to some conjectures about awareness.

The secondary argument is made below, that that some conditions for new collective self aware processes may be developed due to economic benefits of stratified technology.

II.1. Potential impact: Earth orbit satellite deployment and management drives telecommunications technology, since Earth orbit resources is the key component to very large scale commercial interests.

Major new methodologies that impact Earth orbit satellite deployment and management could suddenly and radically reduce costs and increase value for existing and planed satellite systems. The industry is looking for break-through technology and methodology.

II.2. Current practice: From a review of the literature, we assume that satellites now share only a few inter-satellite data transmissions. We assume that most data transmissions are between a ground station and individual satellites. In some cases, high level task and goal oriented management of satellite clusters is accomplished manually and one satellite at a time. This is done on the ground using a variety of expensive software systems.

II.3. Limitations of current practice: The management of multiple satellites in a single task is costly due to the amount of software engineering that is required to make the satellites function within a common goal. The knowledge acquisition needed to manage complex distributed and yet highly isolated (autonomous) systems is often implicit in nature, and thus not readily captured by traditional expert systems.

For example, collective risk assessment and validation of projected consequences is often only partially reducible to computational processes. The non-reduction can be due to scenarios with incomplete information or to inconsistency of information due to information having an unknown context and perhaps different scenarios. Different scenarios are established implicitly by the physical and locality differences between satellites. Computation with scenarios assumes that the raw data is in fact correct, and this is sometimes also not the case.

One would think that automatic ephemeris generation would be a relatively simple collaborative task. However, it is possible to see how scenarios generated by a single satellite might make collective ephemeris generation difficult. One satellite may have multiple errors in its sighting and orientation mechanisms and be communicating with perhaps a dozen other satellites. In this cluster, there may be two of three other sources of misinformation. The collective must process this misinformation and determine the source of the error. This requires a conjectural process and can lead to catastrophic consequences as the system attempts to correct malfunctioning sensors, algorithms, or transmission devices.

Section III. Contributing technology and methodology

Whereas traditional expert systems will provide a degree of assistance, their limitations are well understood.

III.1. Limitations: Intrinsic limitations should condition technology and methodology within a broader architecture that is being proposed.

Model-based reasoning, case based reasoning, diagnostic reasoning and the methods from the field of intelligent agents can each have limitations that are similar to those found with a traditional expert system. These methods also have strengths that should be capitalized on in a selective fashion.

The primary weaknesses are in the following areas.

The above statement of three classes of weakness is a generalization of the three stated weaknesses of expert systems.

III.2. Strengths: The strengths of model-based reasoning is that the model provides an commonly available artifact that allows humans to make correspondences to mechanical, algorithmic and field realities. The artifact allows the discussion of goals and objectives between humans. In some cases, the model allows an automatic (autonomous) adjustment of operational and control parameters according to performance measures.

Software supporting diagnostic reasoning can capture the enumeration of characteristic case reasoning and procedures. These characteristic procedures often have the function of solving a problem easily and quickly by moving the system states from a known condition to a known condition.

IV. Automatic analysis using data mining and knowledge management:

Data mining and knowledge management terminology will be used to assist in communicating the details of the proposed system.

The proposed system has three functional components.

IV.1. A structural data base: All available sources of information, are to be retrieved and relevant sections placed into a structural database of two classes of knowledge artifacts, one being statistical and the other categorical.

Intelligent search agents, having exploratory capability, will carry out the extraction of statistical invariance in data. Some, but not all, data mining will occur on board spacecraft, but much, or all, of to the knowledge management will occur Earth side. A prototype for an exploratory data mining module to be used in on-board processing will be designed for implementation.

Statistical invariance will be discovered and coded into a distributed structural database. Categorical artifacts will be generated via a user interface. System wide database management and control will be done from a central computer resource located Earth side.

The system is to have a special knowledge base containing artifacts (organized into a control language) that "sign" or "point" to general knowledge based on notions of concepts, properties, relationships, and functional dependencies. This knowledge base has concept space representations of the kinds of events experienced by individual satellites and clusters. The development of the knowledge base will use knowledge engineering practices.

IV.2. A reasoning system: A highly innovative and advanced reasoning system has been prototyped based on a tri-level voting procedure. Tri-level reasoning develops knowledge of causes of various events, their consequences, possible ways of avoidance of failures or optimization of recourses, for neutralization or compensation; or, on the contrary, activation of such events, as well as knowledge of event mutual compatibility or incompatibility (synchronicity or diachronicity).

IV.3. A data base of cases: The third key component of the system is a data base of cases. This database stores historical analogs and precedents of historical phenomena, events, conflicts and historically justified ways of solving problems involving similarity analysis.

IV.4. Special inference engines: For real time use, these three system components each have solvers, or special inference engines. This follows a model which has been developed most directly by the Applied Semiotics School in the Soviet Union and later in Russia.

There are three classes of solvers. The first solver, S1, interprets factual data from the structural data base and builds a full set of compatible events, using the computational argumentation of Quasi Axiomatic Theory (QAT) where it uses the knowledge of event compatibility / incompatibility and J. S. Mill's logics. This argumentation is simplified in the 'voting procedure". The compatible events are represented in the form of a rough set and a corresponding "policy assignment" (see technical discussion.

A second solver S2 makes a forecast of situational evolution. This special inference engine determines the set of possible consequences of the full set produced by the first solver, S1. For this purpose, S2 uses properties, relationships and functional dependencies stored in the knowledge base. In theory, S2 can build a full set of compatible events for any new predicted state developed by the engine S1. In case there is a conflict in the predicted state or it contains a conflicting set of events, the third solver S3 looks for appropriate case (cases) in the case base and gives this information to human operators.

If a case has been found, then the solution associated with this case in DBC is chosen to suggest this solution to humans. If there is more than one case, a human user forms a new solution out of partial case solutions, by means of a computer/user interface and second order cybernetic system for construction and elimination of rules and inference paths. The solution is then tested for compatibility of its components - by the S1 solver.

Following the QAT model, the system may have an additional module that models the system behavior over time. This temporal logic module takes into account the consequences of decisions being made, and adjusts the decision making to achieve a particular goal.

V. The relationship between data compression and data understanding:

Data compression involves the discovery of patterns of invariance that can be used to reduce the bits required to transmit data from one point to another point. In many cases, it is necessary that the transmission be lossless, and that error correcting codes provide a validation that the data received is exactly the same as the data transmitted.

Some transmissions require only a lossy outcome, and in this case the size of the data stream can be reduced an order of magnitude or more. In lossy transmission, it is often also required that patterns of invariance be discovered and perhaps stored as models of the statistical frequencies of prototypes.

Lossy compression is used as a means to understand the relevant or meaning of data, including images. This methodology is called "image understanding', even though the 'understanding' only occurs when results of computations as observed by a human.

If the object of understanding is to understand the distribution of symbol sets for the purpose of lossless compression, then we have an ideal situation. This primary situation will be explored in Phase I. However, the same artifacts used in compression will be demonstrated to have high value in data understanding as related to a simulated collective ephemeris generation (see technical section). A second application of the artifacts will be used in a simulated geographical terrain imagine understanding task (see technical section).

VI. Conclusions:

VI.1. Developing and maintaining a collective agreement: Our approach is to develop a tri-level artifact database with common evolution of statistical and categorical artifacts. The approach is based on a model of human perception and on the notion of stratified reasoning. Both the grounding of the tri-level architecture in research from human memory and perception research, and the notational system for stratified reasoning will be given in brief in the proposal (see technical section).

The proposed architecture will demonstrate how to maintain a common agreement between agents that are distributed in location and contextual setting. The agreement is developed in an evolutionary way using low cost opportunities to share scenarios from one agent to another. The scenarios are generated from an aggregation of statistical artifacts (data invariance) into an assignment of categories of experience. The categorization of "experience" provides a measure of context. In critical situations the shared experiences are used to validate decisions and assess the risk of these decisions to the future usefulness of the cluster members.

Both classes of knowledge artifacts, statistical type artifacts and categorical type artifacts, are modified and distributed within the collective. This property provides a type of "collective substructure" that is shared in common between the members of the cluster.