Orion Philosophy and Rationale Knowledge Is Structure If

Orion Philosophy and Rationale

“Knowledge Is Structure” If it really is structure, what sort of structure is it? We are asserting that it is active dynamic undirected structure

A Simple Structure The structure can propagate a wide range of entities through its connections, and the operators can operate on analytic or experiential information, and the structure can change itself. • Visible • Undirected • Dynamically Extensible • Controllable Existence

Logical Surface Statements are written on a logical surface a=b+c

Building Structures We can combine small logical structures into larger structures without concern for how they will later be used. The directions of information flow in the structure are dynamic IF a + b = c THEN d + e = f

Argument from Similarity Real Neurons (not artificial ones) • Switching • Complex messages • Back connections • Changing connections • Storing of memories • Massively parallel activity Network Operators • Switching • Complex messages • Undirected • Changing connections • Storing of memories • Quasi-parallel activity

But Real Neurons Are Directed How can the fluidity of knowledge be built on directed structure? Back-connections throughout the neuronal structure can hide this physical directionality operating at a lower level, just as many small tasks in a queue can hide most of the limitations of a single process

Argument from Function A mental model has to represent a fast-changing world. Its attributes are no accident. The connections in different areas may be different, but the substrate is universal. A knowledge model has to represent a changing world. Can its substrate also be universal, so there are no boundaries between different aspects?

Argument from Utility - One An algorithm/data structure has one part, the data structure, that is visible and easy to change, one part, the algorithm, that is invisible to the machine and hard to change - a stream of instructions. An active structure is all of it visible all of the time. Changing the structure changes its behaviour.

Argument from Utility - Two One algorithm/data structure does not easily merge with another - each has been built for a purpose. One active structure easily merges with another - the phasing lies in the structure, so connecting new structure changes the phasing to suit.

Argument from Visibility Procedures on a stack are invisible to everything but the stack handler. Nothing else can see what is happening and respond to it - the machine is blind to its own workings. This prevents quasi-parallelism. States being propagated in a network are always visible - anything that can be connected to a point of interest can respond to change or influence it, down to the limit of operator granularity.

Argument from Dynamism A=B+C Static structure X = SUM(List) Dynamic structure The quake struck early Thursday. Ultimate dynamism All knowledge has dynamic aspects - aspects we can’t avoid. If a paradigm can handle the most dynamic, it can handle the least dynamic or the static on its ear.

Nowhere to Start This is typical - knowledge is used everywhere no start, no end, lots of loops - it is definitely not a DAG Attempts to enforce some predetermined structure quickly result in irrelevance

Orion Principles • Destroy no inference in the transformation from text to structure • Discover universal substrate by continual extension of problem space • The activity is only in the structure, not outside it • Invisible activity (within operators) is minimised by fine granularity • Accrete the structure where simple operators are inadequate

Examples

Project Management Development projects have a life of their own. There is uncertainty in what you are doing, and whether and when you will be doing it. Critical Path Method doesn’t allow you to plan what to do, only when to do it - the activities in a knowledge model have both logical and existential control, and the design model can sit in the same model as the activities and resources, all made out of the same stuff and communicating with each other

Reinsurance Simulation Household The structure generated for Commercial Property (each column represents a policy) Generated Commercial Property Claims A Surplus treaty Claims paid after the Surplus recoveries Risk Excess treaty Claims paid after the Surplus and Risk. Xs Combine with Household Claims after Quota Share (into Catastrophe treaty) The structure of the reinsurance program is in the form that insurance people understand it – claims flow into a reinsurance treaty, which recovers some of the payments. What remains can be combined and flow into another treaty. The structure is easily modifiable and extensible because it is self-phasing

Combining Knowledge Domains Airframe Performance Avionics Assemble Pieces of Knowledge Into an Active Object Which Itself Can be Assembled. . .

Extracting Meaning from Text Section b(ii). An occupies relation, with Tenant as a subject and portion as an object, and a logical control structure controlling 2 pay relations. Portion is part (contained in) Space, which is the local dictionary object for Swing Space. The occupy relation gets its Time. Period from the Extended Swing Space Term – the new Time. Period of the occupy relation. The Start. Date of both pay relations is 01/09/2005.

Linking In Bioinformatics The structure is used to understand the text then the text is used to extend the structure

How Practical Is The Approach • Creation of model is fast if knowledge is available • Incremental growth of model topology suits typical knowledge acquisition cycle • Large simulation models have similar execution times to more simplistic programmatic versions • Dynamic Constraint Reasoning models have similar solving times to other more static methods

But Is It Easy In one way, creating a knowledge structure is an iron discipline - unlike a program, effects can only occur through connections - so connections must be made and states propagated - no lazy reaching out to assign a value. And yet, is there another way - how can we be sure we are in the correct state unless everything that is relevant can see and contribute to the current state.

Why Do This As applications move towards the use of knowledge, the limitations of a single point of control machine, with so little in its focus, become increasingly burdensome. Still limited to a single point, the propagation of states through network connections, the continuous visibility, the fine granularity and the self-modifiability increase the size of the apparent focus and permit us to move closer to the full use of knowledge.