2013 Scientific Research and Experimental Development tax credit claim
T661 - Part 2 – Project Information
Section B – Experimental development
240 - What technological advancements were you trying to achieve? (Maximum 350 words)
General purpose learning and thinking software for autonomous control of robots based on artificial neural networks (ANN).
- Advancements in the architecture of a hierarchical structure for ANN nodes.
- The use of binary neurons (binons) with reinforcement learning.
- Network growth from the addition of nodes for parallel and sequential pattern recognition.
- Conversion of graduated sensor readings (sub-symbolic) into symbolic stimuli.
- Transfer of short term memory traces into long term memory.
- Learnt action habits integrated into an associated hierarchical behavior network.
- Parallel execution of subconscious action habits.
242 - What technological obstacles/uncertainties did you have to overcome to achieve the advancements described in line 240? (Maximum 350 words)
Current robot control software is not general purpose. It is specifically developed to function in a predefined environment with predefined tasks to be performed. Learning capability, if present, is limited to this environment. General purpose artificial intelligence architectures exist but are not designed for robot control i.e. they are not grounded on sensory input and device activation. Examples include ACT-R, SOAR, CLARION, LIDA, SIGMA and HTM. DRAMA, MAXSON and Brook’s COG are experimental architectures designed for robot control but have not been applied with any success. This research is developing a new architecture to solve the problem. However there are no hierarchical ANNs based on binons that are feed-forward and use reinforcement learning. There are also no ANNs that grow nodes in a hierarchy for each new object or sequence recognized or for each new action habit learnt.
244 – What work did you perform in the tax year to overcome the technological obstacles/uncertainties described in Line 242? (Summarize the systematic investigation) (Maximum 700 words)
Approach: Increase the complexity of the ANN structure and algorithms to process the requirements at greater complexity while still handling the lower complexity features. Run regression tests on already working lower complexity features. Determine its success at learning and thinking based on the observation of its actions in artificial test environments simulated in software and by inspection of its internal memory traces and processes. More details about the research are available at www.adaptroninc.com
Areas of research performed this year - Two pieces of software are being crafted;
A) In the area of object and sequence recognition from multi-sensor senses producing graduated (ratio scale) and discrete (symbolic / nominal) stimulus readings using a hierarchy of binons. Each new version of this software is then integrated into B.
B) In the area of short term and long term memory, learning, thinking and a hierarchical action habit structure.
In area A:
- Added an algorithm to find, represent and count repeating patterns.
- Attempted object recognition independent of the complexity of their parts.
- Added the logic to recognize rotations, reflections and inversions and then removed it after realizing these should be learnt rather than built-in.
- Applied the Weber – Fechner Law to convert graduated (ratio scale) sensor readings into symbolic (nominal) representation.
- Worked on gap recognition to obtain Gestalt laws of grouping for parts.
- Came up with three different ways to group the areas on a two dimensional image for pattern recognition.
- Used the change in acceleration of intensity readings to detect edges and borders.
In area B:
- Modified the practice mode for action habits with directed attention.
- Changed the criteria for action habit creation and update.
- Developed a technique for recognizing expected and unexpected stimuli while practicing or performing learned action habits.