Event: TRIZ Trends 2016 - at GE John F. Welch Technology Center, Bangalore, INDIA, Tuesday – April 12, 2016.

The Agenda is now Available for the day

Inventing in an Expanding Frontier of Ignorance - Extended Abstract

Inventing in an Expanding Frontier of Ignorance –

TRIZ for Information-era Artificial Intelligence and Systems with a Mind

Extended Abstract

Chances are remote that when Feynman was delivering his famous lectures on physics in 1962 and wrote “we do not yet know all the basic laws: there is an expanding frontier of ignorance” in USA, he would have heard of Altshuller, the Soviet prisoner released after Stalin’s death from Siberian confinement, who was discovering the laws of evolution of technical systems through painstaking analysis of thousands of patents which was to result in the Theory of inventive problem solving (TRIZ in Russian parlance). It is indeed the peculiar fate of our world that multiple directions are taken for exploration and development in decoupled social, political and cultural systems – as USA and USSR were after World War II – yet the key to “knowing” or creating “new knowledge” continues to be an evolutionary approach – rather than design, direction and planning. 

Feynman describes that although the sole judge of scientific truth is experiment giving us hints to the laws underlying our world, yet imagination is needed to “create from these hints the great generalizations – to guess and then to experiment to check again whether we have made the right guess.” He further states that laws of nature are approximate and, “… at each stage it is worth learning what is not known, how accurate it is, how it fits into everything else and how it may be changed when we learn more” [1]. This is indeed the process of continuous learning and understanding through an evolutionary process.  However, despite its discovery by Darwin, 150 years back, acceptance of evolution by natural selection as a possible model of explanation of progress of cosmos is relatively a recent phenomenon [2]. As Ridley states in [3], “the way the human history is taught can therefore mislead, because it places far too much emphasis on design, direction and planning, and far too little on evolution”. He further states that, “… to see past the illusion of design, to see the emergent, unplanned, inexorable and beautiful process of change that lies underneath”.

One of the earlier recognition of evolution due to ingenuity of human mind reflected in the successful inventions described in patents and technological knowledge was discovered and explained by the Theory of Inventive Problem Solving (TRIZ). This resulted in discovery of laws of evolution of technical systems which became the basis of classical TRIZ. As per Altshuller the purpose of evolution of technical systems was to achieve the “Ideal system”. Technical systems exist or are created to perform a function. The ideal technical system describes the fulfillment of the function with reduction in number of the elements of technical system that actuates the function. TRIZ and its laws of evolution of technical systems discovered in the era of physical systems – the era of machines – explained the fulfillment of function in an ideal way. The algorithm of achieving ideality was to avoid the costly “trial and error” approaches of evolution – small local changes in multiple copies of the same system which get reflected in the “subsequent generations of system”. TRIZ proposed to jump the costly “trial and error” and focused on ideality and jump many avoidable generations of unnecessary random stages.

Three Eras – Machine, Information, and Mind

From the era of machines – dominated by automobile – which incidentally became the model for classical TRIZ – we are rapidly moving in the era of information. Our understanding of “information” – its nature and its deeper manifestations – is increasing day by day. When we started investigating the applicability of laws of system evolution as described by TRIZ in the current era of information, we realized that TRIZ, understandably missed the key ingredient, i.e., information, embedded in modern technical systems and environment in which these technical systems will be operating.

We discovered that human beings by collectively evolving their technical systems, are trying to make each technical system as close to a human being as possible – or at least a model of human being and its environment based on the current understanding of the world (for example, understanding of laws of physics and chemistry in making an automobile) and the current understanding of the system called the human being. As man understands the world around it as well as its body and its mind, it wants to create an “ideal man” or at least an idealized human of all technical systems it is creating. This is an unexpected discovery and may take the readers used to classical TRIZ, sometime to accept it [4].

Increasing Intelligence of Technical Systems

Given our understanding of the current information era and how technical systems are evolving from their predominantly physical characteristics into information enriched technical systems, we propose in this paper a new law – the law of increasing intelligence of technical systems.

We are in the era of information today. This is an era that has replaced the era of machines that started with industrial revolution. This era of information is giving us systems that are becoming increasingly intelligent. From the dumb systems that were responding to inputs to perform specific functions, we have evolved to guided systems and smart systems of the information era. The next stage of evolution of technical systems is increasingly becoming clear as we are seeing emergence of brilliant systems which we predict will become genius systems. By 2050 AD, the world will have technical systems with more intelligence than biological intelligence – predicted as the Singularity. From information era we are now entering rapidly into the era of mind. This we call as law of increasing intelligence of technical systems.

In the current Information Era, TRIZ needs to update its definition of a technical system to include “information”. The goal of system evolution should be to create ideal systems that attain perfect information to actualize the underlying system functions.

In the emerging era of Mind, TRIZ should be about ideas and thoughts. How can any technical system process and actuate ideas? The ideal technical system will be able to generate and actuate perfect ideas to attain system functions needed in real time or even ahead of time.

Lines of System Intelligence

In the era of information and coming era of mind of technical systems we have proposed a new law of evolution of technical systems using TRIZ way of exploration and creating new knowledge. We call this the Law of Increasing Intelligence of Technical systems. We further propose that TRIZ should incorporate this new law to the existing laws of evolution of technical systems as the current information era and future mind era of technical systems will require new ways and concepts to invent systems that will have more information and in future will have the fourth fundamental we call the mind as their most prominent components.

In the talk we will explain the Lines of system intelligence under the law of increasing intelligence of technical systems that we have uncovered in our exploration so far. These are lines for –

(a) Increasing levels of sensing and detecting,

(b) Increasing levels of processing data, information and knowledge,

(c) Increasing levels of Learning,

(d) Increasing levels of choice –making,

(e) Increasing levels of concept-creation,

(f) Increasing levels of uncertainty, randomness and vagueness

Final Point:

The Artificial Intelligence(AI) and Artificial Super Intelligence (ASI) debate going on in the world today misses out on one key point on “intelligence” – the intelligence need not be “conscious” or “aware”. Non-consciousness intelligence is what we are calling artificial intelligence and that perhaps will continue in the near future and may exceed biological intelligence as per “singularity” by 2045-2055. However, the “artificial consciousness” really has no clarity – unless we claim the cloning of human beings to be the creation of artificial consciousness.

References

[1] The Feynman Lectures on Physics

[2] Steven J. Dick and Mark L. Lupisella (Ed.), Cosmos & Culture – Cultural Evolution in a Cosmic Context, NASA report number, NASA SP-2009-4802, 2009.

[3] Ridley M., The Evolution of Everything – How New Ideas Emerge, Harper Collins, London, 2015.

[4] Bhushan, N., Law of increasing Intelligence of Technical Systems, accessed on 18^th March 2016 http://aitriz.org/triz-articles/inside-triz/596-law-of-increasing-intelligence-of-technical-systems

 

Intelligent Weapon Power Scores for C5ISR Combat Force

Modeling and Evaluation of Weapons Systems –

Intelligent Weapon Power Scores

One of the most complex problems is to create a mathematical model of combat between two military forces. This is further impacted by the increasing impact of changes in technology and new-age combat systems that are emerging continuously. Traditionally, weapon systems were modeled to include four key capabilities in defining a score or a number to indicate key capability of the weapon system. For example, weapon power scores described in our book Strategic Decision Making – Applying the Analytic Hierarchy Process, Springer UK, 2004. The capabilities that were considered are - lethality, self-protection, operability and integration of combat systems with other systems. However, we have seen a remarkable evolution of technical systems in the last three decades with extreme capabilities in information and information processing i.e., computing, becoming the key in the modern combat systems. We describe this trend as law of increasing intelligence of technical systems. In the light of this law, we propose that weapon systems capability needs to be evaluated and scored by including three new capabilities – that is – information processing, decision-making and system learning. This leads us to propose a new metric and methodology termed intelligent weapon power (iWPS) scores to evaluate any weapon system on a same capability plane. This concept of iWPS can be used for static force comparisons, in war simulations, war games and combat scenario evaluation for the new world.

Weapon Power Scores

There exist various techniques for force comparison taking into account quality or effectiveness of weapons besides the quantity of weapons held by opposing forces. These were commonly referred to as Fire Power Scores (FPS) methodologies. Basic idea in FPS methodologies is to assign a numerical value to different weapons indicating their war-making capability. The aggregated product of quantity and fire­power scores of various weapons in a force gives the Force Strength (FS) of the force. Various firepower score methodologies have been developed on the basis of expert judgment, historical data analysis and combat simulation. Some of these methodologies are Weapon Effectiveness Index (WEI)/Weapon Unit Value (WUV) which is based on expert   judgment, Potential Anti-Potential (PAP) Method which uses combat simulations, and Operational Lethality Index (OLI) based on historical data analysis, etc.

                Most of the FPS methodologies for so called static analyses give less importance to other factors such as self-protection capability of weapon system, ability to operate in all weather conditions and at night time, etc., We proposed augmentation of FPS with survivability of the weapon system to give a realistic picture of combat potential of a force. The new score was termed Weapon Power Score (WPS). Later in our book Weapon Power Scores were modified and extended to incorporate other factors such as on-board self-protection capability, operability and ability to communicate with other weapon systems through Command, Control, Communications, and Intelligence (C3I) links.  Expert judgement has been used for the evaluation of WPS.

The evaluation of WPS involves assigning a numerical value to each weapon system of Armed Forces of Adversaries indicating its combat effectiveness. The methodology to evaluate WPS of various weapon systems is given  below. WPS is defined as

WPS = Operational Lethality Index (OLI) ´  (1+Self Protection Index (SPI)) ´

            (1+ Operability Index (OI)) ´ (1+Integration Index (II))                                     (1)          

               

Lethality of a weapon is expressed as Operational Lethality Index (OLI). In this method, empirical formulae are provided for determining these indices. The method divides all weapon systems into two broad categories, namely mobile fighting machines and non-mobile fighting machines.  For non-mobile weapons the OLI is defined as a function of various factors such as Rate of Fire, Number of Potential Targets per Strike, Range Factor, etc. For mobile fighting machines, the OLI is calculated by adding the separately calculated OLIs of all of the weapons on the mobile fighting  machine and  multiplying this result by several performance factors. The methodology  is valid only for land warfare with close air support for army operations.

Besides lethality, it is observed that other factors such as on-board self-protection, operability and capability to get integrated with C3I system also plays important roles in weapon effectiveness. Weapon capability is enhanced if the weapon has characteristics that improve their  ability to survive in the battle. The characteristics may include on-board radars, decoys/chaff, Electronic Counter Measures (ECM), Electronic Support Measures (ESM),  armour protection etc. There are certain on-board systems that enhance the self-protection capability of the weapon system. This is reflected in the Self-Protection Index (SPI). The SPI  takes value in the range (0,1), is added to 1 and the result  is multiplied with OLI. The multiplication indicates that the lethality of the weapon system is considered to be contributing to effectiveness and the extent to which the weapon system has on-board self-protection capabilities improves its effectiveness. The value of SPI for various weapon systems also takes into account the environment in which the weapon system will be operating. Thus, for example, SPI for infantry weapons is considered as 0.85 as it has been observed the infantry casualties in a battle are usually 10% to 15%.  The effectiveness of a weapon system also depends upon its ability to operate in adverse weather/environment conditions and night-time operations. This is reflected in the Operability Index (OI) of the weapon system.  The OI indicates the ability of the weapon system to operate in adverse weather and night-time operations. The value of OI is chosen from the interval (0,1), added to 1 and is multiplied to the product of  OLI and (1+ SPI) indicating the fact that a weapon which can operate only under ideal conditions will have effectiveness as OLI ´ (1+ SPI) (i.e., OI = 0). However, if the weapon can operate in extreme conditions as well then its effectiveness  is enhanced i.e., OI > 0 indicating the flexibility of the weapon system. Another factor enhancing effectiveness of weapon system is their ability to get integrated in the C3I system. This factor depends upon the communication links of the weapon system with various Surveillance systems and Command and Control systems. This is represented as the Integration Index (II) of the weapon system. The value of II is also chosen from the interval   (0,1).

 
For the new network centric combat force - the so called C5ISR force (Command Control Communication Computers Combat Intelligence Surveillance and Reconnaissance) force we propose the Intelligent Weapon Power Score (iWPS) which augments the WPS with three more capability assessment - information processing, decision making and learning and foresight. The new iWPS is a function of 7 capabilities

This is as per the law of increasing intelligence of technical systems (Reference] , the combat systems are embedded with more and more intelligence to become increasingly capable, agile, autonomous and collectively synergistic.  In the new network-centric warfare scenarios, the weapon power will have three more capabilities – information processing, decision-making and learning. The intelligent weapon power score (iwps) needs to model these three new capabilities along with the four key capabilities of the weapon power scores. 

Will post more details once the paper is published ....