arXiv subjects

Mark Burgess

Publications and source records attributed to Mark Burgess.

At least 19 recordsLinked to original sources

arXiv2026

Legal Responsibilities Using Autonomous Agents For Artificial Intelligence

Recent incidents involving Artificial Intelligence (AI) agents, which were reported escaping their containment `unintentionally' to gain unauthorized access, pose looming questions about who or what should be held legally responsible for resultant criminal or negligent damage. As the independent capabilities of agents expand, Promise Theory suggests a systematic method to resolve these questions, based on the Downstream Principle for causal influence. Responsibility can easily be expanded to include AI agents where tracing responsibility becomes impactical, and agents' freedoms to act can be limtied by policy choices.

cs.AI↗

arXiv2026

Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents

I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics -- as well as how Promise Theory supplements solutions, helping to avoid probability's pitfalls, which include non-local coordination, calibrating, and normalizing probabilistic computations. The role of boundary conditions in constraining allowed states and selecting decision thresholds is a form of promise, and agent alignment provides a scalable definition of intent. Autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information, despite uncertainty that works to maximize it. The use of Promise Theory involves some research challenges as well as stylistic preferences.

cs.AI↗

arXiv2025

$\gamma(3,4)$ `Attention' in Cognitive Agents: Ontology-Free Knowledge Representations With Promise Theoretic Semantics

The semantics and dynamics of `attention' are closely related to promise theoretic notions developed for autonomous agents and can thus easily be written down in promise framework. In this way one may establish a bridge between vectorized Machine Learning and Knowledge Graph representations without relying on language models implicitly. Our expectations for knowledge presume a degree of statistical stability, i.e. average invariance under repeated observation, or `trust' in the data. Both learning networks and knowledge graph representations can meaningfully coexist to preserve different aspects of data. While vectorized data are useful for probabilistic estimation, graphs preserve the intentionality of the source even under data fractionation. Using a Semantic Spacetime $\gamma(3,4)$ graph, one avoids complex ontologies in favour of classification of features by their roles in semantic processes. The latter favours an approach to reasoning under conditions of uncertainty. Appropriate attention to causal boundary conditions may lead to orders of magnitude compression of data required for such context determination, as required in the contexts of autonomous robotics, defence deployments, and ad hoc emergency services.

cs.AI↗

arXiv2025

On The Role of Intentionality in Knowledge Representation: Analyzing Scene Context for Cognitive Agents with a Tiny Language Model

Since Searle's work deconstructing intent and intentionality in the realm of philosophy, the practical meaning of intent has received little attention in science and technology. Intentionality and context are both central to the scope of Promise Theory's model of Semantic Spacetime, used as an effective Tiny Language Model. One can identify themes and concepts from a text, on a low level (without knowledge of the specific language) by using process coherence as a guide. Any agent process can assess superficially a degree of latent `intentionality' in data by looking for anomalous multi-scale anomalies and assessing the work done to form them. Scale separation can be used to sort parts into `intended' content and `ambient context', using the spacetime coherence as a measure. This offers an elementary but pragmatic interpretation of latent intentionality for very low computational cost, and without reference to extensive training or reasoning capabilities. The process is well within the reach of basic organisms as it does not require large scale artificial probabilistic batch processing. The level of concept formation depends, however, on the memory capacity of the agent.

cs.AI↗

arXiv2025

Agent Semantics, Semantic Spacetime, and Graphical Reasoning

Some formal aspects of the Semantic Spacetime graph model are presented, with reference to its use for directed knowledge representations and process modelling. A finite $\gamma(3,4)$ representation is defined to form a closed set of operations that can scale to any degree of semantic complexity. The Semantic Spacetime postulates bring predictability with minimal constraints to pathways in graphs. The ubiquitous appearance of absorbing states in any partial graph means that a graph process leaks information. The issue is closely associated with the issue of division by zero, which signals a loss of closure and the need for manual injection of remedial information. The Semantic Spacetime model (and its Promise Theory) origins help to clarify how such absorbing states are associated with boundary information where intentionality can enter.

cs.AI↗

arXiv2023

Neuroscience needs Network Science

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.

q-bio.NC↗

arXiv2022

Continuous Integration of Data Histories into Consistent Namespaces

We describe a policy-based approach to the scaling of shared data services, using a hierarchy of calibrated data pipelines to automate the continuous integration of data flows. While there is no unique solution to the problem of time order, we show how to use a fair interleaving to reproduce reliable `latest version' semantics in a controlled way, by trading locality for temporal resolution. We thus establish an invariant global ordering from a spanning tree over all shards, with controlled scalability. This forms a versioned coordinate system (or versioned namespace) with consistent semantics and self-protecting rate-limited versioning, analogous to publish-subscribe addressing schemes for Content Delivery Network (CDN) or Name Data Networking (NDN) schemes.

cs.DC↗

arXiv2022

On The Scale Dependence and Spacetime Dimension of the Internet with Causal Sets

A statistical measure of dimension is used to compute the effective average space dimension for the Internet and other graphs, based on typed edges (links) from an ensemble of starting points. The method is applied to CAIDA's ITDK data for the Internet. The effective dimension at different scales is calibrated to the conventional Euclidean dimension using low dimensional hypercubes. Internet spacetime has a 'foamy' multi-scale containment hierarchy, with interleaving semantic types. There is an emergent scale for approximate long range order in the device node spectrum, but this is not evident at the AS level, where there is finite distance containment. Statistical dimension is thus a locally varying measure, which is scale-dependent, giving an visual analogy for the hidden scale-dependent dimensions of Kaluza-Klein theories. The characteristic exterior dimensions of the Internet lie between 1.66 +- 0.00 and 6.12 +- 0.00, and maximal interior dimensions rise to 7.7.

cs.SI↗

arXiv2021

Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity

Developing shopping experiences that delight the customer requires businesses to understand customer taste. This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity. Shopping activity is represented as a bipartite graph. This graph is refined by applying sparsity-based statistical learning methods. These methods are interpretable and reveal insights about customers' preferences as well as products driving revenue to the store.

cs.LG↗

arXiv2021

On system rollback and totalised fields

In system operations it is commonly assumed that arbitrary changes to a system can be reversed or `rolled back', when errors of judgement and procedure occur. We point out that this view is flawed and provide an alternative approach to determining the outcome of changes. Convergent operators are fixed-point generators that stem from the basic properties of multiplication by zero. They are capable of yielding a repeated and predictable outcome even in an incompletely specified or `open' system. We formulate such `convergent operators' for configuration change in the language of groups and rings and show that, in this form, the problem of convergent reversibility becomes equivalent to the `division by zero' problem. Hence, we discuss how recent work by Bergstra and Tucker on zero-totalised fields helps to clear up long-standing confusion about the options for `rollback' in change management.

cs.DC↗

arXiv2020

Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II) : Establishing the Geometry of Invariant Concepts, Themes, and Namespaces

Given a pool of observations selected from a sensor stream, input data can be robustly represented, via a multiscale process, in terms of invariant concepts, and themes. Applying this to episodic natural language data, one may obtain a graph geometry associated with the decomposition, which is a direct encoding of spacetime relationships for the events. This study contributes to an ongoing application of the Semantic Spacetime Hypothesis, and demonstrates the unsupervised analysis of narrative texts using inexpensive computational methods without knowledge of linguistics. Data streams are parsed and fractionated into small constituents, by multiscale interferometry, in the manner of bioinformatic analysis. Fragments may then be recombined to construct original sensory episodes---or form new narratives by a chemistry of association and pattern reconstruction, based only on the four fundamental spacetime relationships. There is a straightforward correspondence between bioinformatic processes and this cognitive representation of natural language. Features identifiable as `concepts' and `narrative themes' span three main scales (micro, meso, and macro). Fragments of the input act as symbols in a hierarchy of alphabets that define new effective languages at each scale.

cs.AI↗

arXiv2020

Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I) : Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape

The problem of extracting important and meaningful parts of a sensory data stream, without prior training, is studied for symbolic sequences, by using textual narrative as a test case. This is part of a larger study concerning the extraction of concepts from spacetime processes, and their knowledge representations within hybrid symbolic-learning `Artificial Intelligence'. Most approaches to text analysis make extensive use of the evolved human sense of language and semantics. In this work, streams are parsed without knowledge of semantics, using only measurable patterns (size and time) within the changing stream of symbols -- as an event `landscape'. This is a form of interferometry. Using lightweight procedures that can be run in just a few seconds on a single CPU, this work studies the validity of the Semantic Spacetime Hypothesis, for the extraction of concepts as process invariants. This `semantic preprocessor' may then act as a front-end for more sophisticated long-term graph-based learning techniques. The results suggest that what we consider important and interesting about sensory experience is not solely based on higher reasoning, but on simple spacetime process cues, and this may be how cognitive processing is bootstrapped in the beginning.

cs.AI↗

arXiv2020

Spacetime-Entangled Networks (I) Relativity and Observability of Stepwise Consensus

Consensus protocols can be an effective tool for synchronizing small amounts of data over small regions. We describe the concept and implementation of entangled links, applied to data transmission, using the framework of Promise Theory as a tool to help bring certainty to distributed consensus. Entanglement describes co-dependent evolution of state. Networks formed by entanglement of agents keep certain promises: they deliver sequential messages, end-to-end, in order, and with atomic confirmation of delivery to both ends of the link. These properties can be used recursively to assure a hierarchy of conditional promises at any scale. This is a useful property where a consensus of state or `common knowledge' is required. We intentionally straddle theory and implementation in this discussion.

cs.DC↗

arXiv2020

Information and Causality in Promise Theory

The explicit link between Promise Theory and Information Theory, while perhaps obvious, is laid out explicitly here. It's shown how causally related observations of promised behaviours relate to the probabilistic formulation of causal information in Shannon's theory, and thus clarify the meaning of autonomy or causal independence, and further the connection between information and causal sets. Promise Theory helps to make clear a number of assumptions which are commonly taken for granted in causal descriptions. The concept of a promise is hard to escape. It serves as proxy for intent, whether a priori or by inference, and it is intrinsic to the interpretations of observations in the latter.

cs.MA↗

arXiv2020

Candidate Software Process Flaws for the Boeing 737 Max MCAS Algorithm and Risks for a Proposed Upgrade

By reasoning about the claims and speculations promised as part of the public discourse, we analyze the hypothesis that flaws in software engineering played a critical role in the Boeing 737 MCAS incidents. We use promise-based reasoning to discuss how, from an outsider's perspective, one may assemble clues about what went wrong. Rather than looking for a Rational Alternative Design (RAD), as suggested by Wendel, we look for candidate flaws in the software process. We describe four such potential flaws. Recently, Boeing has circulated information on its envisaged MCAS algorithm upgrade. We cast this as a promise to resolve the flaws, i.e. to provide a RAD for the B737 Max. We offer an assessment of B-Max-New based on the public discourse.

cs.CY↗

arXiv2019

Locality, Statefulness, and Causality in Distributed Information Systems (Concerning the Scale Dependence Of System Promises)

Several popular best-practice manifestos for IT design and architecture use terms like `stateful', `stateless', `shared nothing', etc, and describe `fact based' or `functional' descriptions of causal evolution to describe computer processes, especially in cloud computing. The concepts are used ambiguously and sometimes in contradictory ways, which has led to many imprecise beliefs about their implications. This paper outlines the simple view of state and causation in Promise Theory, which accounts for the scaling of processes and the relativity of different observers in a natural way. It's shown that the concepts of statefulness or statelessness are artifacts of observational scale and causal bias towards functional evaluation. If we include feedback loops, recursion, and process convergence, which appear acausal to external observers, the arguments about (im)mutable state need to be modified in a scale-dependent way. In most cases the intended focus of such remarks is not terms like `statelessness' but process predictability. A simple principle may be substituted in most cases as a guide to system design: the principle the separation of dynamic scales. Understanding data reliance and the ability to keep stable promises is of crucial importance to the consistency of data pipelines, and distributed client-server interactions, albeit in different ways. With increasingly data intensive processes over widely separated distributed deployments, e.g. in the Internet of Things and AI applications, the effects of instability need a more careful treatment. These notes are part of an initiative to engage with thinkers and practitioners towards a more rational and disciplined language for systems engineering for era of ubiquitous extended-cloud computing.

cs.DC↗

arXiv2019

From Observability to Significance in Distributed Information Systems

To understand and explain process behaviour we need to be able to see it, and decide its significance, i.e. be able to tell a story about its behaviours. This paper describes a few of the modelling challenges that underlie monitoring and observation of processes in IT, by human or by software. The topic of the observability of systems has been elevated recently in connection with computer monitoring and tracing of processes for debugging and forensics. It raises the issue of well-known principles of measurement, in bounded contexts, but these issues have been left implicit in the Computer Science literature. This paper aims to remedy this omission, by laying out a simple promise theoretic model, summarizing a long standing trail of work on the observation of distributed systems, based on elementary distinguishability of observations, and classical causality, with history. Three distinct views of a system are sought, across a number of scales, that described how information is transmitted (and lost) as it moves around the system, aggregated into journals and logs.

cs.MA↗

arXiv2019

Koalja: from Data Plumbing to Smart Workspaces in the Extended Cloud

Koalja describes a generalized data wiring or `pipeline' platform, built on top of Kubernetes, for plugin user code. Koalja makes the Kubernetes underlay transparent to users (for a `serverless' experience), and offers a breadboarding experience for development of data sharing circuitry, to commoditize its gradual promotion to a production system, with a minimum of infrastructure knowledge. Enterprise grade metadata are captured as data payloads flow through the circuitry, allowing full tracing of provenance and forensic reconstruction of transactional processes, down to the versions of software that led to each outcome. Koalja attends to optimizations for avoiding unwanted processing and transportation of data, that are rapidly becoming sustainability imperatives. Thus one can minimize energy expenditure and waste, and design with scaling in mind, especially with regard to edge computing, to accommodate an Internet of Things, Network Function Virtualization, and more.

cs.DC↗