Cognition for Technical Systems

Cognitive technical systems (CTS) are information processing systems equipped with artificial sensors and actuators, integrated and embedded into physical systems and acting in a physical world

They differ from other technical systems as they perform cognitive control and have cognitive capabilities

The cognitive capabilities will result in systems of higher reliability, flexibility, adaptivity, and better performance


Researchers studying human sensorimotor control have found convincing empirical evidence for the use of Bayes estimation and cost function enabled control mechanisms in natural movement control

we cannot expect that generally all information processing mechanisms optimized for the perceptual apparatus, the brain, and the limbs of humans or non-human primates will apply, without modification, to the control of CTSs

Learning and motor control for reaching and grasping provide a good case in point.While motor control in natural systems takes up to 100ms to receive motion feedback, high end industrial manipulators execute feedback loops at 1000Hz with a delay of 0.5ms. In contrast to robot arms, control signals for muscles are noisy and muscles take substantial amounts of time to produce the required force. On the other hand, antagonistic muscle groups support the achievement of equilibrium states. Thus, where in natural systems predictive models of motion are required because of the large delay of feedback signals, robot arms can perform the same kind of motions better by using fast feedback loops without resorting to prediction.

COTESYS investigates the cognition in technical systems in terms of the cognition-based perception-action closed loop. All research within COTESYS is dedicated to real-time performance of this control loop, in the real world.

In order to achieve the needed synergies, the coupling of the different cognitive capabilities must be much more intense and interconnected For example, the system can learn to plan and plan to learn. It can learn to plan more reliably and efficiently and also plan in order to acquire informative experiences to learn from. 

  • Perception

Perception is the acquisition of information about the environment and the body of an actor. In cognitive science models, part of the information received by the receptors is processed at higher levels in order to produce task-relevant information. This is done by recognizing, classifying, and locating objects perception is often framed as a probabilistic estimation problem and the estimated states are often transformed into symbolic representations that enable the systems to communicate and reason about what they perceive.

  • Action

Action is the process of generating behavior to change the world and to achieve some objectives of the acting entity. Natural cognitive systems use internal forward models to predict the consequences of motor signals to account for delays in the computation process and filtering out uninformative incoming sensory information. This cognitive science view can be contrasted to control theory, where behavior is specified in terms of control rules. Control rules for feedback control are derived from accurate mathematical dynamical system models. The design of control rules aims at control systems that are controllable, stable, and robust and can thereby provably satisfy given performance requirements. Action theories in artificial intelligence typically abstract from many dynamical aspects of actions and behavior in order to handle more complex tasks. Powerful computational models have been developed to rationally select the best actions (based on decision theory criteria), to learn skills and action selection strategies from experience, and to perform action aware control.

  • Knowledge

Knowledge (Models) in cognitive science is conceived to consist of both declarative and procedural knowledge. 

Declarative knowledge is recognizing and understanding factual information known about objects, ideas, and events in the environment It also contains the inter-relationships between

objects, events, and entities in the environment.

Procedural knowledge is information regarding how to execute a sequence of operations. In cognitive science various models have been proposed as part of computational models of motor control and learning to explain behavior of human and primate behavior in empirical studies. Most prominent are the forward and backward models of actions for the prediction of the actions’ effects and sensory consequences and for the optimization of skills. Graphical models have been proposed to explain the acquisition of causal knowledge with younger children. In control systems, various mathematical models, such as differential equations or automata that capture the evolution of dynamical systems, are used. Research in artificial intelligence has produced powerful representations for joint probability distributions and symbolic knowledge representation mechanisms. It has developed the mechanisms to endow CTSs with encyclopedic and common sense knowledge.

  • Learning

Learning is the process of acquiring information, and, respectively, the reorganization of information that results in new knowledge. The learned knowledge can relate to skills, attitudes, and values and can be acquired through study, experience, or being taught, the cognitive science view. Learning causes a change of behavior that is persistent, measurable, and specified. It is a process that depends on experience and leads to long-term changes in behavior. In control theory, adaptive control investigates control algorithms in which one or more of the parameters varies in real time, to allow the controller to remain effective in varying process conditions. Another key learning mechanism is the identification of parameters in mathematical models. In artificial intelligence, a large variety of information processing methods for learning have been developed. These mechanisms include classification learners, such as decision tree learners or support vector machines, function approximators, such as artificial neural networks, sequence learning algorithms, and reinforcement learners that determine optimal action selection strategies for uncertain situations. The learning algorithms are complemented by more general approaches such as data mining and integrated learning systems (see DARPA Initiative – grand challenges).

  • Reasoning

Reasoning is a cognitive process by which an individual or system may infer a conclusion from an assortment of evidence, or from statements of principles. In the cognitive sciences reasoning processes are typically studied in the context of complex problem solving tasks, such as solving student problems, using protocol analysis methods (“think aloud”). In the engineering sciences specific reasoning mechanisms for prediction tasks, such as Bayesian filtering, are employed and studied. Other reasoning tasks are solved in the system design phase by the system engineers, where control rules are proven to be stable. The resulting systems have no need for execution time reasoning, because of their guaranteed behavior envelope. Artificial intelligence has developed a variety of reasoning mechanisms, including causal, temporal, spatial, and teleological reasoning, which enables CTSs to solve dynamically changing, interfering, and more complex tasks.

  • planning

planning is a process of generating (possibly partial) representations of future behavior, prior to the use of such plans, to constrain or control current behavior. It comprises reasoning about the future in order to generate, revise, or optimize the intended course of action. In the artificial intelligence view plans are considered to be control programs that can be executed, be reasoned about, and be manipulated.