A Non-Standard Semantics for Kahn Networks in Continuous Time

In a seminal article, Kahn has introduced the notion of process network and given a semantics for those using Scott domains whose elements are (possibly infinite) sequences of values. This model has since then become a standard tool for studying distributed asynchronous computations. From the beginning, process networks have been drawn as particular graphs, but this syntax is never formalized. We take the opportunity to clarify it by giving a precise definition of these graphs, that we call nets. The resulting category is shown to be a fixpoint category, i.e. a cartesian category which is traced wrt the monoidal structure given by the product, and interestingly this structure characterizes the category: we show that it is the free fixpoint category containing a given set of morphisms, thus providing a complete axiomatics that models of process networks should satisfy. We then use these tools to build a model of networks in which data vary over a continuous time, in order to elaborate on the idea that process networks should also be able to encompass computational models such as hybrid systems or electric circuits. We relate this model to Kahn's semantics by introducing a third model of networks based on non-standard analysis, whose elements form an internal complete partial order for which many properties of standard domains can be reformulated. The use of hyperreals in this model allows it to formally consider the notion of infinitesimal, and thus to make a bridge between discrete and continuous time: time is "discrete", but the duration between two instants is infinitesimal. Finally, we give some examples of uses of the model by describing some networks implementing common constructions in analysis.Comment: 201

Relational Semantics of Non-Deterministic Dataflow

We recast dataflow in a modern categorical light using profunctors as a generalization of relations. The well known causal anomalies associated with relational semantics of indeterminate dataflow are avoided, but still we preservemuch of the intuitions of a relational model. The development fits with the view of categories of models for concurrency and the general treatment of bisimulation they provide. In particular it fits with the recent categorical formulation of feedback using traced monoidal categories. The payoffs are: (1) explicit relations to existing models and semantics, especially theusual axioms of monotone IO automata are read off from the definition of profunctors, (2) a new definition of bisimulation for dataflow, the proof of the congruence of which benefits from the preservation properties associated with open maps and (3) a treatment of higher-order dataflow as a biproduct,essentially by following the geometry of interaction programme

Towards a proof of the Kahn principle for linear dynamic networks

We consider dynamic Kahn-like data flow networks, i.e. networks consisting of deterministic processes each of which is able to expand into a subnetwork. The Kahn principle states that such networks are deterministic, i.e. that for each network we have that each execution provided with the same input delivers the same output. Moreover, the principle states that the output streams of such networks can be obtained as the smallest fixed point of a suitable operator derived from the network specification. This paper is meant as a first step towards a proof of this principle. For a specific subclass of dynamic networks, linear arrays of processes, we define a transition system yielding an operational semantics which defines the meaning of a net as the set of all possible interleaved executions. We then prove that, although on the execution level there is much nondeterminism, this nondeterminism disappears when viewing the system as a transformation from an input stream to an output stream. This result is obtained from the graph of all computations. For any configuration such a graph can be constructed. All computation sequences that start from this configuration and that are generated by the operational semantics are embedded in it

Beyond rules: The next generation of expert systems

The PARAGON Representation, Management, and Manipulation system is introduced. The concepts of knowledge representation, knowledge management, and knowledge manipulation are combined in a comprehensive system for solving real world problems requiring high levels of expertise in a real time environment. In most applications the complexity of the problem and the representation used to describe the domain knowledge tend to obscure the information from which solutions are derived. This inhibits the acquisition of domain knowledge verification/validation, places severe constraints on the ability to extend and maintain a knowledge base while making generic problem solving strategies difficult to develop. A unique hybrid system was developed to overcome these traditional limitations

Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control

Towards Web 2.0 Schools: Rethinking the Teachers

This paper aims at analyzing the Web 2.0 based distance education in the K-12 schools as an emerging phenomenon that catalyzes a new educational reform all over the world. Some pre-Web 2.0 best practice examples are analyzed in order to draw the main findings in the paper. The teacher’s professional qualification designed to meet the new challenges is considered as a key problem for a successful penetration of this phenomenon in the schools. It is emphasized on the importance of designing a life-long teacher training strategy adapted to the new achievements in the technology enhanced learning research and the new learning theories. Building social skills and competencies appropriate to work in a Web 2.0 based learning environment and other global ‘social software’ is recommended to be included both in the school curricula and the corresponding teacher development curricula. Such skills and competencies should penetrate the curricula of any life-long learning initiative dedicated to the citizens of the information society