Intelligent Control of Vehicles: Preliminary Results on the Application of Learning Automata Techniques to Automated Highway System

We suggest an intelligent controller for an automated vehicle to plan its own trajectory based on sensor and communication data received. Our intelligent controller is based on an artificial intelligence technique called learning stochastic automata. The automaton can learn the best possible action to avoid collisions using the data received from on-board sensors. The system has the advantage of being able to work in unmodeled stochastic environments. Simulations for the lateral control of a vehicle using this AI method provides encouraging results

A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme

-Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for example, as inferred from an Oracle (which possibly computes the derivatives), suffices to achieve the optimization—without actually explicitly computing any derivatives. The SPL can be described in terms of a LM (algorithm) attempting to locate a point on a line. The LM interacts with a random environment which essentially informs it, possibly erroneously, if the unknown parameter is on the left or the right of a given point. Given a current estimate of the optimal solution, all the reported solutions to this problem effectively move along the line to yield updated estimates which are in the neighborhood of the current solution.1 This paper proposes a dramatically distinct strategy, namely, that of partitioning the line in a hierarchical tree-like manner, and of moving to relatively distant points, as characterized by those along the path of the tree. We are thus attempting to merge the rich fields of stochastic optimization and data structures. Indeed, as in the original discretized solution to the SPL, in one sense, our solution utilizes the concept of discretization and operates a uni-dimensional controlled random walk (RW) in the discretized space, to locate the unknown parameter. However, by moving to nonneighbor points in the space, our newly proposed hierarchical stochastic searching on the line (HSSL) solution performs such a controlled RW on the discretized space structured on a superimposed binary tree. We demonstrate that the HSSL solution is orders of magnitude faster than the original SPL solution proposed by - ommen. By a rigorous analysis, the HSSL is shown to be optimal if the effectiveness (or credibility) of the environment, given by $p$ , is greater than the golden ratio conjugate. The solution has been both analytically solved and simulated, and the results obtained are extremely fascinating, as this is the first reported use of time reversibility in the analysis of stochastic learning. The learning automata extensions of the scheme are currently being investigated

Theory and application of learning automata.

Although the theoretical performance of many learning automata has been considered, the practical operation of these automata has received far less attention. This work starts with the construction of two action Tsetlin and Krylov automata. The performance of these automata has been measured in stationary and non-stationary environments. The operation of a hierarchical automaton controlling the memory size of a Tsetlin automaton is also investigated. Two new automata are proposed with the aim of avoiding the operational disadvantages of the Tsetlin automaton. These automata have been tested using a computer simulation and, in addition, theoretical performance results have been calculated and compared with results for Tsetlin, Krylov and Lri automata. A model of a non-autonomous environment is simulated and its operation analysed theoretically. A more accurate model is analysed, and its operation with a Lri automaton examined and compared to theoretical predictions. The requirements for learning automata to operate successfully in non-autonomous environments are considered and it is shown that the Lrp and Lri automata do not converge to the optimum for a non-autonomous environment. Three automata are proposed, which are designed to operate in non-autonomous environments. Their performances are compared to those of the Lrp and Lri automata. The operation of automata in a hierarchical learning system and in cooperative and competitive games is considered. In these situations the performance of the new automata is compared to that of the Lrp and Lri automata. Finally, two applications of learning automata are investigated. The first considers the Tsetlin allocation scheme, gives a modification that increases the performance and makes a comparison with a scheme using other learning automata. The second involves the selection of a processor in a multiprocessor computer system and compares a scheme using learning automata with a fixed scheduling discipline

Study of decentralised decision models in distributed environments

Many of today's complex systems require effective decision making within uncertain distributed environments. The central theme of the thesis considers the systematic analysis for the representation of decision making organisations. The basic concept of stochastic learning automata provides a framework for modelling decision making in complex systems. Models of interactive decision making are discussed, which result from interconnecting decision makers in both synchronous and sequential configurations. The concepts and viewpoints from learning theory and game theory are used to explain the behaviour of these structures. This work is then extended by presenting a quantitative framework based on Petri Net theory. This formalism provides a powerful means for capturing the information flow in the decision-making process and demonstrating the explicit interactions between decision makers. Additionally, it is also used for the description and analysis of systems that axe characterised as being concurrent, asynchronous, distributed, parallel and/ or stochastic activities. The thesis discusses the limitations of each modelling framework. The thesis proposes an extension to the existing methodologies by presenting a new class of Petri Nets. This extension has resulted in a novel structure which has the additional feature of an embedded stochastic learning automata. An application of this approach to a realistic decision problem demonstrates the impact that the use of an artificial intelligence technique embedded within Petri Nets can have on the performance of decision models

Multiple Stochastic Learning Automata for Vehicle Path Control in an Automated Highway System

This paper suggests an intelligent controller for an automated vehicle planning its own trajectory based on sensor and communication data. The intelligent controller is designed using the learning stochastic automata theory. Using the data received from on-board sensors, two automata (one for lateral actions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments, unlike adaptive control methods or expert systems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging result

Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond

The acceleration race of digital computing technologies seems to be steering toward impasses -- technological, economical and environmental -- a condition that has spurred research efforts in alternative, "neuromorphic" (brain-like) computing technologies. Furthermore, since decades the idea of exploiting nonlinear physical phenomena "directly" for non-digital computing has been explored under names like "unconventional computing", "natural computing", "physical computing", or "in-materio computing". This has been taking place in niches which are small compared to other sectors of computer science. In this paper I stake out the grounds of how a general concept of "computing" can be developed which comprises digital, neuromorphic, unconventional and possible future "computing" paradigms. The main contribution of this paper is a wide-scope survey of existing formal conceptualizations of "computing". The survey inspects approaches rooted in three different kinds of background mathematics: discrete-symbolic formalisms, probabilistic modeling, and dynamical-systems oriented views. It turns out that different choices of background mathematics lead to decisively different understandings of what "computing" is. Across all of this diversity, a unifying coordinate system for theorizing about "computing" can be distilled. Within these coordinates I locate anchor points for a foundational formal theory of a future computing-engineering discipline that includes, but will reach beyond, digital and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now (March 2021) been published as "Toward a generalized theory comprising digital, neuromorphic, and unconventional computing" in the new open-access journal Neuromorphic Computing and Engineerin