Actryx Ltd
Mindful Action
Transparency in Decision Support,
Process Control and Optimization
About Us
Actryx offers real-time solutions for decision support and process optimization. Combining a rich set of powerful state-of-the-art concepts results in decision architectures that enable seamless interoperation between the human decision-maker and its AI-driven decision-support.
Company Overview
Mission
Actryx joins in with the global effort to make augmented and automatic decision-making as transparent as possible so that humans will have trust in their decision support and do not hesitate to take responsibility for even the most complex decisions. Our mission aims at counterbalancing a trend where
Machine Learning, as automated AI modelling methodology that by-passes the cost for understanding underlying process complexities, often produces "black box" models the oblique (unexplainable) inner logic of which is not amiable to human reasoning (i.e. causal assessment) - their application raises fundamental questions as to the responsibility for the decision process. With AI technology rapidly expanding in variety, power, potential and risks, Actryx continues to capitalize on this development, yet with special focus on trends that enforce transparency, rationality and causality, providing its clients with state-of-the-art, AI-inspired solutions that they can have confidence in, and take responsibility for.
Services
We provide consultancy and software development in all technology areas mentioned below. For a non-exhaustive list:
We provide consultancy and software development in all technology areas mentioned below. For a non-exhaustive list:
- Stakeholder-based information and decision architectural design
- Decision support software solutions
- Model building (from first principles and ML/data-driven)
- Model optimization and identification
- Process simulation and optimization
- Planning solutions
- Online anomaly detection, mitigation and prevention
- General AI consultancy
All software solutions can be offered on-premise or as a software-as-a-service (SAAS).
Technology
This section explains some basic concepts that can help to analyse a decision-making problem at high level.
Decision Science in Context
Decision science is the problem of finding an algorithmic policy that makes the best possible decision (optimal action) so as to achieve specific objectives subject to specific rules (constraints). Essentially being a optimization problem, Its systematic development has gone through four major phases (so far): (1) starting in the 1940s with cybernetics as studying the control of dynamic systems (i.e. systems with feedback loops); (2) during 1960s with the systematic systems and control of linear dynamic systems, leading to a rich theory of stable control algorithms; (3) during the 1980s with studying the control (and its complexity) of nonlinear dynamic systems, and with neurofuzzy control an early example of an AI-inspired heuristic control (i.e. decision) solution; (4) after 2010 with reinforcement learning providing an algorithmic policy to learning a system model about the behaviour of its environment whilst concurrently trying to control it . Though developments through all stages are based on optimization principles, provable safety guarantees, and risk assessments are much harder to obtain for stages (3) and later, Yet problems typical for these later stages, such as social, economic, health, defence, and biological problems, have immense interest, and only with making the effort to understand their complexities, potential application and risks shall we be able to master and manipulate them, responsibly.
Decision science is the problem of finding an algorithmic policy that makes the best possible decision (optimal action) so as to achieve specific objectives subject to specific rules (constraints). Essentially being a optimization problem, Its systematic development has gone through four major phases (so far): (1) starting in the 1940s with cybernetics as studying the control of dynamic systems (i.e. systems with feedback loops); (2) during 1960s with the systematic systems and control of linear dynamic systems, leading to a rich theory of stable control algorithms; (3) during the 1980s with studying the control (and its complexity) of nonlinear dynamic systems, and with neurofuzzy control an early example of an AI-inspired heuristic control (i.e. decision) solution; (4) after 2010 with reinforcement learning providing an algorithmic policy to learning a system model about the behaviour of its environment whilst concurrently trying to control it . Though developments through all stages are based on optimization principles, provable safety guarantees, and risk assessments are much harder to obtain for stages (3) and later, Yet problems typical for these later stages, such as social, economic, health, defence, and biological problems, have immense interest, and only with making the effort to understand their complexities, potential application and risks shall we be able to master and manipulate them, responsibly.
Distributed Decision-Making
Often decisions result from the collaboration of independent decision-makers (i.e. agents); e.g. think of human organizations. This distributed decision-making concept is quite general in that (1) each decision node can potentially be fulfilled by a human or a machine (depending on the level of routine), and (2) may function as a modularization scheme for decision-making problems that are too complex to be solved atomically. If all sub-node decisions are routine to the degree that they all can be handled by a machine (i.e. no human-in-the-loop), we then speak of automation.
Often decisions result from the collaboration of independent decision-makers (i.e. agents); e.g. think of human organizations. This distributed decision-making concept is quite general in that (1) each decision node can potentially be fulfilled by a human or a machine (depending on the level of routine), and (2) may function as a modularization scheme for decision-making problems that are too complex to be solved atomically. If all sub-node decisions are routine to the degree that they all can be handled by a machine (i.e. no human-in-the-loop), we then speak of automation.
Optimization vs Decision-Making
Decision science is essentially an optimization problem. However, there is an important distinction between one-shot decisions
and sequential decision-making, the first being a (single) decision to be made only once (i.e. model optimization) and the latter being a real-time process
where decisions are made over successive time points (i.e. actions optimization).
Opportunistic vs Mission-Critical Decision-Making
For opportunistic problems
the performance, safety and risks are measured according to acceptable statistics (e.g. a 2 percent failure rate of a medical diagnosis, or a 40 percent failure rate of a financial trading algorithm) - their design usually is probabilistic
(i.e. deliberately exhibiting statistical variation). For mission-critical problems, however, failure is catastrophic (i.e. loss of mission) and tolerances are typically extremely small (e.g. railway and aviation traffic control systems, moon landings, ...) - their designs must be completely deterministic.
