Design optimisation using advanced artificial intelligent system coupled to hybrid-game strategies

One of the main aims in artificial intelligent system is to develop robust and efficient optimisation methods for Multi-Objective (MO) and Multidisciplinary Design (MDO) design problems. The paper investigates two different optimisation techniques for multi-objective design optimisation problems. The first optimisation method is a Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The second method combines the concepts of Nash-equilibrium and Pareto optimality with Multi-Objective Evolutionary Algorithms (MOEAs) which is denoted as Hybrid-Game. Numerical results from the two approaches are compared in terms of the quality of model and computational expense. The benefit of using the distributed hybrid game methodology for multi-objective design problems is demonstrated.

Emergent task-specific object semantics through distributed experience networks

Autonomous development of sensorimotor coordination enables a robot to adapt and change its action choices to interact with the world throughout its lifetime. The Experience Network is a structure that rapidly learns coordination between visual and haptic inputs and motor action. This paper presents methods which handle the high dimensionality of the network state-space which occurs due to the simultaneous detection of multiple sensory features. The methods provide no significant increase in the complexity of the underlying representations and also allow emergent, task-specific, semantic information to inform action selection. Experimental results show rapid learning in a real robot, beginning with no sensorimotor mappings, to a mobile robot capable of wall avoidance and target acquisition.

Personalised information gathering and recommender systems : techniques and trends

With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-the-art tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from a technological and social perspective. Aiming to promote high quality research in order to overcome these challenges, this paper provides a comprehensive survey on the recent work and achievements in the areas of personalised web information gathering and recommender systems. The report covers concept-based techniques exploited in personalised information gathering and recommender systems.

Developing Co-operating Legal Knowledge Based Systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work supplements rule-based reasoning with case based reasoning and intelligent information retrieval. This research, specifies an approach to the case based retrieval problem which relies heavily on an extended object-oriented / rule-based system architecture that is supplemented with causal background information. Machine learning techniques and a distributed agent architecture are used to help simulate the reasoning process of lawyers. In this paper, we outline our implementation of the hybrid IKBALS II Rule Based Reasoning / Case Based Reasoning system. It makes extensive use of an automated case representation editor and background information.

The IKBALS project: multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval. When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers. Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process. Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions. The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.

Fuzzy-DL perception for multi-robot systems

For future planetary robot missions, multi-robot-systems can be considered as a suitable platform to perform space mission faster and more reliable. In heterogeneous robot teams, each robot can have different abilities and sensor equipment. In this paper we describe a lunar demonstration scenario where a team of mobile robots explores an unknown area and identifies a set of objects belonging to a lunar infrastructure. Our robot team consists of two exploring scout robots and a mobile manipulator. The mission goal is to locate the objects within a certain area, to identify the objects, and to transport the objects to a base station. The robots have a different sensor setup and different capabilities. In order to classify parts of the lunar infrastructure, the robots have to share the knowledge about the objects. Based on the different sensing capabilities, several information modalities have to be shared and combined by the robots. In this work we propose an approach using spatial features and a fuzzy logic based reasoning for distributed object classification.

Distributed optimization in power networks and general multi-agent systems

The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

Efficient electronic implementations of adaptive systems for ambient intelligence environments

183 p.

Rationales of holonic manufacturing systems in leather industry

This paper presents an insight into leather manufacturing processes, depicting peculiarities and challenges faced by leather industry. An analysis of this industry reveals the need for a new approach to optimize the productivity of leather processing operations, ensure consistent quality of leather, mitigate the adverse health effects in tannery workers exposed to chemicals and comply with environmental regulation. Holonic manufacturing systems (HMS) paradigm represent a bottom-up distributed approach that provides stability, adaptability, efficient use of resources and a plug and operate functionality to the manufacturing system. A vision of how HMS might operate in a tannery is illustrated presenting the rationales behind its application in this industry. © 2013 Springer-Verlag.

A framework for distributed intelligent automation systems developments

This chapter proposes a simple and pragmatic framework that has been developed for classifying and analyzing developments in distributed automation and information systems - especially those that have been labelled intelligent systems for different reasons. The framework dissects the different stages in the standard feedback process and assesses distribution in terms of the level of granularity of the organization that is being considered. The framework has been found to be useful in comparing and assessing different distributed industrial control paradigms and also for examining common features of different development projects - especially those that might be sourced from different sectors or domains. © Springer-Verlag Berlin Heidelberg 2013.

Optimisation of Smart Grid performance using centralised and distributed control techniques

A massive change is currently taking place in the manner in which power networks are operated. Traditionally, power networks consisted of large power stations which were controlled from centralised locations. The trend in modern power networks is for generated power to be produced by a diverse array of energy sources which are spread over a large geographical area. As a result, controlling these systems from a centralised controller is impractical. Thus, future power networks will be controlled by a large number of intelligent distributed controllers which must work together to coordinate their actions. The term Smart Grid is the umbrella term used to denote this combination of power systems, artificial intelligence, and communications engineering. This thesis focuses on the application of optimal control techniques to Smart Grids with a focus in particular on iterative distributed MPC. A novel convergence and stability proof for iterative distributed MPC based on the Alternating Direction Method of Multipliers is derived. Distributed and centralised MPC, and an optimised PID controllers' performance are then compared when applied to a highly interconnected, nonlinear, MIMO testbed based on a part of the Nordic power grid. Finally, a novel tuning algorithm is proposed for iterative distributed MPC which simultaneously optimises both the closed loop performance and the communication overhead associated with the desired control.

Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.