The dynamic clusters method in nonhierarchical clustering

Given a finite setE R ⁿ, the problem is to find clusters (or subsets of similar points inE) and at the same time to find the most typical elements of this set. An original mathematical formulation is given to the problem. The proposed algorithm operates on groups of points, called samplings (samplings may be called multiple centers or cores); these samplings adapt and evolve into interesting clusters. Compared with other clustering algorithms, this algorithm requires less machine time and storage. We provide some propositions about nonprobabilistic convergence and a sufficient condition which ensures the decrease of the criterion. Some computational experiments are presented.     

The impact of artificial intelligence on leisure

Two views of AI in leisure and the work-place and two views of society are discussed. There is a conceptualisation of AI systems enhancing people in their work and leisure and another of AI automata which tends to degrade and replace human activity. Researchers tend to resolve into Optimists who work within a micro-sociological view and see AI systems as inevitable and beneficent. Others are Pessimists who adopt a macro-sociological view and see AI in its automata role and deliterious social consequences. These polarised perspectives must be integrated as only enhancing AI is socially acceptable.     

Adding a temporal dimension to a logic system

We introduce a methodology whereby an arbitrary logic system L can be enriched with temporal features to create a new system T(L). The new system is constructed by combining L with a pure propositional temporal logic T (such as linear temporal logic with Since and Until) in a special way. We refer to this method as adding a temporal dimension to L or just temporalising L. We show that the logic system T(L) preserves several properties of the original temporal logic like soundness, completeness, decidability, conservativeness and separation over linear flows of time. We then focus on the temporalisation of first-order logic, and a comparison is make with other first-order approaches to the handling of time.     

Modelling structures in generic space, a condition for adaptiveness of monitoring cognitive agent

The adaptiveness of agents is one of the basic conditions for the autonomy. This paper describes an approach of adaptiveness forMonitoring Cognitive Agents based on the notion of generic spaces. This notion allows the definition of virtual generic processes so that any particular actual process is then a simple configuration of the generic process, that is to say a set of values of parameters. Consequently, generic domain ontology containing the generic knowledge for solving problems concerning the generic process can be developed. This lead to the design of Generic Monitoring Cognitive Agent, a class of agent in which the whole knowledge corpus is generic. In other words, modeling a process within a generic space becomes configuring a generic process and adaptiveness becomes genericity, that is to say independence regarding technology. In this paper, we present an application of this approach on Sachem, a Generic Monitoring Cognitive Agent designed in order to help the operators in operating a blast furnace. Specifically, the NeuroGaz module of Sachem will be used to present the notion of a generic blast furnace. The adaptiveness of Sachem can then be noted through the low cost of the deployment of a Sachem instance on different blast furnaces and the ability of NeuroGaz in solving problem and learning from various top gas instrumentation.     

Recognition of handwritten musical notes by a modified Neocognitron

A neural network for recognition of handwritten musical notes, based on the well-known Neocognitron model, is described. The Neocognitron has been used for the what pathway (symbol recognition), while contextual knowledge has been applied for the where (symbol placement). This way, we benefit from dividing the process for dealing with this complicated recognition task. Also, different degrees of intrusiveness in learning have been incorporated in the same network: More intrusive supervised learning has been implemented in the lower neuron layers and less intrusive in the upper one. This way, the network adapts itself to the handwriting of the user. The network consists of a 13×49 input layer and three pairs of simple and complex neuron layers. It has been trained to recognize 20 symbols of unconnected notes on a musical staff and was tested with a set of unlearned input notes. Its recognition rate for the individual unseen notes was up to 93%, averaging 80% for all categories. These preliminary results indicate that a modified Neocognitron could be a good candidate for identification of handwritten musical notes.     

Rapid Concept Learning for Mobile Robots

Concept learning in robotics is an extremely challenging problem: sensory data is often high-dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as opening or wall. The second task is recycling, where the robot has to learn to recognize objects, such as a trash can. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.     

Stable adaptive control for a plant with randomly varying parameters

Adaptive control is considered for a two-dimensional linear discrete-time plant with randomly drifting parameters. The certainty equivalent minimum variance control law along with the projection-like identification algorithm are used. The stability of the parameter estimates and exponential stability of the closed-loop system are proved in the absence of any persistent excitation assumption.     

Enhancing the performance of an agent-based manufacturing system through learning and forecasting

Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through learning from history (based on distributed case-based learning and reasoning) and learning from the future (through system forecasting simulation). Learning from history is used to enhance coordination capabilities by minimizing communication and processing overheads. Learning from the future is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.     

Sound and Complete Qualitative Simulation Needs “Quantitative” Filtering

The AI methodology of qualitative reasoning furnishes useful tools to scientists and engineers who need to deal with incomplete system knowledge during design, analysis, or diagnosis tasks. Qualitative simulators have a theoretical soundness guarantee; they cannot overlook any concrete equation implied by their input. On the other hand, the basic qualitative simulation algorithms have been shown to suffer from the incompleteness problem; they may allow non-solutions of the input equation to appear in their output. The question of whether a simulator with purely qualitative input which never predicts spurious behaviors can ever be achieved by adding new filters to the existing algorithm has remained unanswered. In this paper, we show that, if such a sound and complete simulator exists, it will have to be able to handle numerical distinctions with such a high precision that it must contain a component that would better be called a quantitative, rather than qualitative reasoner. This is due to the ability of the pure qualitative format to allow the exact representation of the members of a rich set of numbers.     

The role of “craft language” in learning “Waza”

The role of craft language in the process of teaching (learning) Waza (skill) will be discussed from the perspective of human intelligence.It may be said that the ultimate goal of learning Waza in any Japanese traditional performance is not the perfect reproduction of the teaching (learning) process of Waza. In fact, a special metaphorical language (craft language) is used, which has the effect of encouraging the learner to activate his creative imagination. It is through this activity that the he learns his own habitus (Kata).It is suggested that, in considering the difference of function between natural human intelligence and artificial intelligence, attention should be paid to the imaginative activity of the learner as being an essential factor for mastering Kata.This article is a modified English version of Chapter 5 of my bookWaza kara shiru (Learning from Skill), Tokyo University Press, 1987, pp. 93–105.     

On characterizing the “knee” of the Pareto curve based on Normal-Boundary Intersection

This paper deals with the issue of generating one Pareto optimal point that is guaranteed to be in a desirable part of the Pareto set in a given multicriteria optimization problem. A parameterization of the Pareto set based on the recently developed normal-boundary intersection technique is used to formulate a subproblem, the solution of which yields the point of maximum bulge, often referred to as the knee of the Pareto curve. This enables the identification of the good region of the Pareto set by solving one nonlinear programming problem, thereby bypassing the need to generate many Pareto points. Further, this representation extends the concept of the knee for problems with more than two objectives. It is further proved that this knee is invariant with respect to the scales of the multiple objective functions.The generation of this knee however requires the value of each objective function at the minimizer of every objective function (the pay-off matrix). The paper characterizes situations when approximations to the function values comprising the pay-off matrix would suffice in generating a good approximation to the knee. Numerical results are provided to illustrate this point. Further, a weighted sum minimization problem is developed based on the information in the pay-off matrix, by solving which the knee can be obtained.     

A Note on the Ohya-Masuda Quantum Algorithm

We analyze the Ohya-Masuda quantum algorithm that solves the so-called satisfiability problem, which is an NP-complete problem of the complexity theory. We distinguish three steps in the algorithm, and analyze the second step, in which a coherent superposition of states (a pure state) transforms into an incoherent mixture presented by a density matrix. We show that, if nonideal (in analogy with nonideal quantum measurement), this transformation can make the algorithm to fail in some cases. On this basis we give some general notions on the physical implementation of the Ohya-Masuda algorithm.     

An effective fuzzy-nets training scheme for monitoring tool breakage

Recent research results show that fuzzy logic and the neural networks systems are very effective in detecting the breakage of cutting tools during machining processes. In the present study, a fuzzy-nets training procedure was developed to build the rule banks to meet the dynamic requirements of machining processes. The system is capable of responding and adapting in real-time to either shut down the machine when a tool fracture occurs or tune the process parameters on-line. The training procedure was validated in a back-up truck problem. Furthermore, two fuzzy-nets systems were combined to serve as a tool breakage detection system for an end milling operation. When the system was evaluated for end milling, the adaptive capability (of the fuzzy-nets system) was shown to enable detection of tool breakage to occur on-line, approaching a real-time base.     

The effects of a “distinct window” screen design on computer-mediated group decision making

An experiment was performed to test a distinct-window conferencing screen design as an electronic cue of social status differences in computer-mediated group decision-making. The screen design included one distinct window to symbolize high-status, and two nondistinct windows to symbolize low-status. The results indicated that the distinct-window screen design did produce status affects in groups of peers making decisions on judgmental problems. Randomly assigned occupants of the distinct window had greater influence on group decisions and member's attitudes than occupants of nondistinct windows.The authors would like to thank Shyam Kamadolli and Phaderm Nangsue, the programmers who developed the software used in this experiment. We would also like to thank the editor and our three anonymous reviewers for exceedingly helpful comments on an earlier draft of this article.     

Rapid Concept Learning for Mobile Robots

Concept learning in robotics is an extremely challenging problem: sensory data is often high dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as opening or wall. The second task is recycling, where the robot has to learn to recognize objects, such as a trash can. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.     

Nonblockability of Switches with the Cayley Graph Structure for Serial Transfer of Data Blocks. Generalized Hypercubes and Multidimensional Grids

A general method of conflictless arbitrary permutation of large data elements that can be divided into a multitude of smaller data blocks was considered for switches structured as the Cayley graphs. The method was specified for arbitrary permutations in the generalized hypercubes and multidimensional grids, and their characteristics were considered.     

Modelling default and likelihood reasoning as probabilistic reasoning

This paper presents a probabilistic analysis of plausible reasoning about defaults and about likelihood. Likely and by default are in fact treated as duals in the same sense as possibility and necessity. To model these four forms probabilistically, a logicQDP and its quantitative counterpartDP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying prepositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.Much of this research was done while at the University of Technology, Sydney, Broadway, NSW, Australia, and some at the Turing Institute, 36 Nth. Hanover Str., Glasgow, Scotland.     

An efficient steering control formulation for the articulated body mobile robot “KR-II”

This paper introduces an efficient steering control method for the articulated body mobile robot Koryu-II (KR-II). KR-II is a real robot, composed of six cylindrical segments linked in series and has a long snake-like appearance. The main issue on KR-II's steering control is, given from a remote human operator the velocity and orientation commands for the foremost segment, to automatically generate joint commands for all the following segments, such that they follow the foremost segment's trajectory. The derived method is based on a trajectory planning scheme in the inertial reference frame, and is feasible for real time computation. It also presents good energy efficiency and trajectory tracking performance characteristics, and can be extended for KR-II's W-Shaped Configuration steering control, which augments the lateral stability of the robot, essential for locomotion over uneven terrain. The validity of these methods are verified by experiments on the mechanical model KR-II.     

Assessing the Changeability of two Object-Oriented Design Alternatives--a Controlled Experiment

An important motivation for the object-oriented paradigm is to improve the changeability of the software, thereby reducing lifetime development costs. This paper describes the results of controlled experiments assessing the changeability of a given responsibility-driven (RD) design versus an alternative control-oriented mainframe (MF) design. According to Coad and Yourdon's OO design quality principles, the RD design represents a good design. The MF design represents a bad design. To investigate which of the designs have better changeability, we conducted two controlled experiments--a pilot experiment and a main experiment. In both experiments, the subjects were divided in two groups in which the individuals designed, coded and tested several identical changes on one of the two design alternatives.The results clearly indicate that the good RD design requires significantly more change effort for the given set of changes than the alternative bad MF design. This difference in change effort is primarily due to the difference in effort required to understand how to solve the change tasks. Consequently, reducing class-level coupling and increasing class cohesion may actually increase the cognitive complexity of a design. With regards to correctness and learning curve, we found no significant differences between the twodesigns. However, we found that structural attributes change less for the RD design than for the MF design. Thus, the RD design may be less prone to structural deterioration. A challenging issue raised in this paper is therefore the tradeoff between change effort and structural stability.