Strategies for creating new informational primitives in minds and machines

Open-endedness is an important goal for designing systems that can autonomously find new and expected solutions to combinatorically-complex and ill-defined problems. Classically, issues of open-ended generation of novelty in the universe have come under the rubric of the problem of emergence. We distinguish two modes of creating novelty: combinatoric (new combinations of existing primitive

Routines and representations at work - observing the architecture of conceptual design

routines, representations, artifacts, product development, workplace observation, evolutionary economics, chip manufacturing

Anthills Built to Order: Automating Construction with Artificial Swarms

PhD thesisSocial insects build large, complex structures, which emerge through the collective actions of many simple agents acting with no centralized control or preplanning. These natural systems motivate investigating the use of artificial swarms to automate construction or fabrication. The goal is to be able to take an unspecified number of simple robots and a supply of building material, give the system a high-level specification for any arbitrary structure desired, and have a guarantee that it will produce that structure without further intervention.In this thesis I describe such a distributed system for automating construction, in which autonomous mobile robots collectively build user-specified structures from square building blocks. The approach preserves many desirable features of the natural systems, such as considerable parallelism and robustness to factorslike robot loss and variable order or timing of actions. Further, unlike insect colonies, it can build particular desired structures according to a high-level design provided by the user.Robots in this system act without explicit communication or cooperation, instead using the partially completed structure to coordinate their actions. This mechanism is analogous to that of stigmergy used by social insects, in which insects take actions that affect the environment, and the environmental state influences further actions. I introduce a framework of "extended stigmergy" in which building blocks are allowed to store, process or communicate information. Increasing the capabilities of the building material (rather than of the robots) in this way increases the availability of nonlocal structure information. Benefits include significant improvements in construction speed and in ability to take advantage of the parallelism of the swarm.This dissertation describes system design and control rules for decentralized teams of robots that provably build arbitrary solid structures in two dimensions. I present a hardware prototype, and discuss extensions to more general structures, including those built with multiple block types and in three dimensions

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 3: ARAMIS overview

An overview of automation, robotics, and machine intelligence systems (ARAMIS) is provided. Man machine interfaces, classification, and capabilities are considered

Artificial ontogenesis: a connectionist model of development

This thesis suggests that ontogenetic adaptive processes are important for generating intelligent beha- viour. It is thus proposed that such processes, as they occur in nature, need to be modelled and that such a model could be used for generating artificial intelligence, and specifically robotic intelligence. Hence, this thesis focuses on how mechanisms of intelligence are specified.A major problem in robotics is the need to predefine the behaviour to be followed by the robot. This makes design intractable for all but the simplest tasks and results in controllers that are specific to that particular task and are brittle when faced with unforeseen circumstances. These problems can be resolved by providing the robot with the ability to adapt the rules it follows and to autonomously create new rules for controlling behaviour. This solution thus depends on the predefinition of how rules to control behaviour are to be learnt rather than the predefinition of rules for behaviour themselves.Learning new rules for behaviour occurs during the developmental process in biology. Changes in the structure of the cerebral 'cortex underly behavioural and cognitive development throughout infancy and beyond. The uniformity of the neocortex suggests that there is significant computational uniformity across the cortex resulting from uniform mechanisms of development, and holds out the possibility of a general model of development. Development is an interactive process between genetic predefinition and environmental influences. This interactive process is constructive: qualitatively new behaviours are learnt by using simple abilities as a basis for learning more complex ones. The progressive increase in competence, provided by development, may be essential to make tractable the process of acquiring higher -level abilities.While simple behaviours can be triggered by direct sensory cues, more complex behaviours require the use of more abstract representations. There is thus a need to find representations at the correct level of abstraction appropriate to controlling each ability. In addition, finding the correct level of abstrac- tion makes tractable the task of associating sensory representations with motor actions. Hence, finding appropriate representations is important both for learning behaviours and for controlling behaviours. Representations can be found by recording regularities in the world or by discovering re- occurring pat- terns through repeated sensory -motor interactions. By recording regularities within the representations thus formed, more abstract representations can be found. Simple, non -abstract, representations thus provide the basis for learning more complex, abstract, representations.A modular neural network architecture is presented as a basis for a model of development. The pat- tern of activity of the neurons in an individual network constitutes a representation of the input to that network. This representation is formed through a novel, unsupervised, learning algorithm which adjusts the synaptic weights to improve the representation of the input data. Representations are formed by neurons learning to respond to correlated sets of inputs. Neurons thus became feature detectors or pat- tern recognisers. Because the nodes respond to patterns of inputs they encode more abstract features of the input than are explicitly encoded in the input data itself. In this way simple representations provide the basis for learning more complex representations. The algorithm allows both more abstract represent- ations to be formed by associating correlated, coincident, features together, and invariant representations to be formed by associating correlated, sequential, features together.The algorithm robustly learns accurate and stable representations, in a format most appropriate to the structure of the input data received: it can represent both single and multiple input features in both the discrete and continuous domains, using either topologically or non -topologically organised nodes. The output of one neural network is used to provide inputs for other networks. The robustness of the algorithm enables each neural network to be implemented using an identical algorithm. This allows a modular `assembly' of neural networks to be used for learning more complex abilities: the output activations of a network can be used as the input to other networks which can then find representations of more abstract information within the same input data; and, by defining the output activations of neurons in certain networks to have behavioural consequences it is possible to learn sensory -motor associations, to enable sensory representations to be used to control behaviour

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Human Swarm Problem Solving

Chapter 4 in Cultural-historical perspectives on collective intelligence In the era of digital communication, collective problem solving is increasingly important. Large groups can now resolve issues together in completely different ways, which has transformed the arts, sciences, business, education, technology, and medicine. Collective intelligence is something we share with animals and is different from machine learning and artificial intelligence. To design and utilize human collective intelligence, we must understand how its problem-solving mechanisms work. From democracy in ancient Athens, through the invention of the printing press, to COVID-19, this book analyzes how humans developed the ability to find solutions together. This wide-ranging, thought-provoking book is a game-changer for those working strategically with collective problem solving within organizations and using a variety of innovative methods. It sheds light on how humans work effectively alongside machines to confront challenges that are more urgent than what humanity has faced before. This title is also available as Open Access on Cambridge Core.Chapter 4 discusses human swarm problem solving as a distinct subtype of CI with biological antecedents in nest siting among honeybees and flocking behavior. Building on recent biological research, this chapter discusses five mechanisms that are also relevant for human swarm problem solving. These mechanisms are decision threshold methods, averaging, large gatherings, heterogeneous social interaction, and environmental sensing. Studies of collective animal behavior show that they often make decisions that build on statistical rules (e.g. averaging, threshold responses). Even when in a group, individuals will often seek and assess information independently of others with the intention of optimizing decisions through the “many wrongs principle” or the “many eyes principle.” Similarly, human ‘wisdom of the crowd’ studies examine similar statistical rules and principles like the importance of making independent contributions. However, while early research on the wisdom of crowds addressed the importance of independent contributions, newer studies also examine the possible positive influence of dependent contributions. The increasing variety of crowdsourcing studies are in this chapter explained with the framework of different swarm mechanisms. In the summary, four basic characteristics of human swarm problem solving are highlighted: predefined problems, pre-specified problem solving procedures, rapid time-limited problem solving, and individual learning.publishedVersio

Computing multi-scale organizations built through assembly

The ability to generate and control assembling structures built over many orders of magnitude is an unsolved challenge of engineering and science. Many of the presumed transformational benefits of nanotechnology and robotics are based directly on this capability. There are still significant theoretical difficulties associated with building such systems, though technology is rapidly ensuring that the tools needed are becoming available in chemical, electronic, and robotic domains. In this thesis a simulated, general-purpose computational prototype is developed which is capable of unlimited assembly and controlled by external input, as well as an additional prototype which, in structures, can emulate any other computing device. These devices are entirely finite-state and distributed in operation. Because of these properties and the unique ability to form unlimited size structures of unlimited computational power, the prototypes represent a novel and useful blueprint on which to base scalable assembly in other domains. A new assembling model of Computational Organization and Regulation over Assembly Levels (CORAL) is also introduced, providing the necessary framework for this investigation. The strict constraints of the CORAL model allow only an assembling unit of a single type, distributed control, and ensure that units cannot be reprogrammed - all reprogramming is done via assembly. Multiple units are instead structured into aggregate computational devices using a procedural or developmental approach. Well-defined comparison of computational power between levels of organization is ensured by the structure of the model. By eliminating ambiguity, the CORAL model provides a pragmatic answer to open questions regarding a framework for hierarchical organization. Finally, a comparison between the designed prototypes and units evolved using evolutionary algorithms is presented as a platform for further research into novel scalable assembly. Evolved units are capable of recursive pairing ability under the control of a signal, a primitive form of unlimited assembly, and do so via symmetry-breaking operations at each step. Heuristic evidence for a required minimal threshold of complexity is provided by the results, and challenges and limitations of the approach are identified for future evolutionary studies

Autonomous scheduling technology for Earth orbital missions

The development of a dynamic autonomous system (DYASS) of resources for the mission support of near-Earth NASA spacecraft is discussed and the current NASA space data system is described from a functional perspective. The future (late 80's and early 90's) NASA space data system is discussed. The DYASS concept, the autonomous process control, and the NASA space data system are introduced. Scheduling and related disciplines are surveyed. DYASS as a scheduling problem is also discussed. Artificial intelligence and knowledge representation is considered as well as the NUDGE system and the I-Space system