Rapprochement of artificial intelligence and dynamics

It is proposed that continuous time is in effect discretized in the brain by dynamic pattern recognition mechanisms in neurons. Time discretization is required to support formal computations in continuous time systems consisting of a large number of components. The ability to perform formal computations is necessary if the system is to execute high level algorithms of the type used in present day artificial intelligence. The weakness of such algorithms is that they work efficiently only when the forms of patterns and objects presented to them are highly constrained. The dynamic mechanisms which discretize the brain's time line also serve to code patterns into constrained forms suitable for high level processing.     

Electricity consumption prediction using artificial intelligence

Central European Journal of Operations Research - The measurement of electricity consumption at 15-minute granularity, including for households, is increasingly mandated in the EU and this also...     

Appraisals of harms and injustice trigger an eerie feeling that decreases trust in artificial intelligence systems

As artificial intelligence (AI) becomes more pervasive, the concern over how users can trust artificial agents is more important than ever before. In this research, we seek to understand the trust formation between humans and artificial agents from the morality and uncanny theory perspective. We conducted three studies to carefully examine the effect of two moral foundations: perceptions of harm and perceptions of injustice, as well as reported wrongdoing on uncanniness and examine the effect of uncanniness on trust in artificial agents. In Study 1, we found perceived injustice was the primary determinant of uncanniness and uncanniness had a negative effect on trust. Studies 2 and 3 extended these findings using two different scenarios of wrongful acts involving an artificial agent. In addition to explaining the contribution of moral appraisals to the feeling of uncanny, the latter studies also uncover substantial contributions of both perceived harm and perceived injustice. The results provide a foundation for establishing trust in artificial agents and designing an AI system by instilling moral values in it.

     

Preface: artificial intelligence in operations management

Annals of Operations Research -     

Impact of artificial intelligence investment on firm value

As artificial intelligence (AI) has recently gained momentum and attention, the interest and investment in AI have also accelerated. However, the impact of AI on firm value is rarely discussed. On the basis of the 119 announcements of 62 listed firms who have invested in AI, this study finds that AI investment has a negative impact on the firms’ market value. The stock prices of the firms decrease by 1.77% on the day of the announcement. Nonmanufacturing firms and firms with weak information technology capabilities or low credit ratings suffer a more negative impact compared with other firms. The findings suggest that investors perceive AI investment announcements to be unwelcome news for the majority of firms. Subsequently, the characteristics affecting the shareholders’ reaction towards AI adoption are presented. This research offers one of the first empirical evidence about the market value of AI and provides a reference for firms interested in investing in AI.

     

A selected artificial intelligence bibliography for operations researchers

We have compiled a selected, classified, and annotated Artificial Intelligence bibliography specifically addressed to an operations research audience. The bibliography includes approximately 450 references from the areas of search (including heuristics and games), automatic deduction (including theorem proving, logic programming, and logical aspects of databases), planning, learning, and knowledge-based systems (with numerous specific applications to management, engineering, science, medicine, and other fields). We have also added a general references section, as well as a special section on Artificial Intelligence/Operations Research interfaces.Supported in part by Air Force through Grant AFOSR #0271.     

Analytic psychology: Source concepts for artificial intelligence

As intelligence technology advances from problem specific to adaptive designs it is conceivable that design concepts will be found in analytic psychology. This psychology features a mechanistic and structured model of human adaptivity from which such concepts may derive. This paper develops this position and illustrates it with three examples: the von Neumann machine, digital simulation of dreams, and digital simulation of the mechanism of the creative process. The simulating algorithms of the last two examples are implemented in Fortran.     

Impact of artificial intelligence adoption on online returns policies

The shift to e-commerce has led to an astonishing increase in online sales for retailers. However, the number of returns made on online purchases is also increasing and have a profound impact on retailers’ operations and profit. Hence, retailers need to balance between minimizing and allowing product returns. This study examines an offline showroom versus an artificial intelligence (AI) online virtual-reality webroom and how the settings affect customers’ purchase and retailers’ return decisions. A case study is used to illustrate the AI application. Our results show that adopting artificial intelligence helps sellers to make better returns policies, maximize reselling returns, and reduce the risks of leftovers and shortages. Our findings unlock the potential of artificial intelligence applications in retail operations and should interest practitioners and researchers in online retailing, especially those concerned with online returns policies and the consumer personalized service experience.

     

A perspective on artificial intelligence: Learning to learn

The classical approach to the acquisition of knowledge in artificial intelligence has been to program the intelligence into the machine in the form of specific rules for the application of the knowledge: expert systems. Unfortunately, the amount of time and resources required to program an expert system with sufficient knowledge for non-trivial problem-solving is prohibitively large. An alternative approach is to allow the machine tolearn the rules based upon trial-and-error interaction with the environment, much as humans do. This will require endowing the machine with a sophisticated set of sensors for the perception of the external world, the ability to generate trial actions based upon this perceived information, and a dynamic evaluation policy to allow it to measure the effectiveness of its trial actions and modify its repertoire accordingly. The principles underlying this paradigm, known ascollective learning systems theory, have already been applied to sophisticated gaming problems, demonstrating robust learning and dynamic adaptivity.The fundamental building block of a collective learning system is thelearning cell, which may be embedded in a massively parallel, hierarchical data communications network. Such a network comprising 100 million learning cells will approach the intelligence capacity of the human cortex. In the not-too-distant future, it may be possible to build a race of robotic slaves to perform a wide variety of tasks in our culture. This goal, while irresistibly attractive, is most certainly fraught with severe social, political, moral, and economic difficulties.This paper was given as an invited talk on the 12th Symposium on Operations Research, University of Passau, September 1987.     

Unwanted noise and vibration control using finite element analysis and artificial intelligence

A mechatronic approach integrating both passive and active controllers is presented in this study to deal with unwanted noise and vibration produced in an automobile wiper system operation. Wiper system is a flexible structure with high order, nonlinear model that is considered as a multi objective control problem since there is a conflict between its functionality quality in wiping and generated unwanted noise and vibration. A passive control technique using multi body system (MBS) model and finite element analysis (FEA) is introduced to identify the potential of the effectiveness of the physical parameters of wiper blade to give appropriate range to reduce the unwanted noise and vibration in the system. While, the significant contribution of active controller is to deal with uncertainties exerted to system within wiper operation. In passive control stage, natural frequencies of a uni-blade automobile wiper are determined using modal testing. Later, a 3-dimensional model of a wiper blade assembly is developed in multi body system design to investigate the good validation test and agreement of the physical behavior of the system in experiment with finite element analysis. Parametric modification via complex eigenvalue is adopted to predict instability of the wiper blade. In active control level, experimental data collected from the wiper system during its operation. A system identification model named nonlinear auto regressive exogenous Elman neural network (NARXENN) is developed for implying the active controller. A bi-level adaptive-fuzzy controller is brought in whose parameters are tuned simultaneously by a multi objective genetic algorithm (MOGA) to deal with the conflict interests in wiper control problem.     

The potential impact of artificial intelligence on the practice of OR

The fantastic claims made by Artificial Intelligence Experts could lead us to think that computers will inevitably have a great impact on disciplines such as OR, even to the extent of displacing them. However, we should question whether there is any evidence to support these claims. This paper considers the present capabilities of computers and looks at the impact that the AI community has had on the practice of OR. It also examines the potential success of current research and lists some of the obstacles to development. The evidence suggests that although AI will provide significant support to the work of OR researchers, the impact of AI on the OR community will not be as great as the claims suggest.     

Mathematical description of artificial sense-of-touch systems

The paper proposes a mathematical formalism for describing artificial sense-of-touch systems. Mathematical models for obtaining, processing, and interpreting tactile information are provided. Problems of encoding and reproducing tactile information are formulated and algorithms for solving these problems are proposed. The problem of interpreting tactile information is considered, and the corresponding simple mathematical model is studied. Within the framework of this simple model, an exact solution of the interpretation problem is obtained for the case of finite deformations, and the insufficiency of the linear method (Hooke’s law) for describing problems of interpreting tactile data is shown. The presentation of mathematical models for the theory of artificial sense-of-touch systems in this paper is the first such detailed presentation in the Russian literature. These models are of interest to mathematicians, mechanicians, physicians, and engineers who construct or use artificial sense-of-touch systems.     

Dynamics of three coupled limit cycle oscillators with application to artificial intelligence

We study a system of three limit cycle oscillators which exhibits two stable steady states. The system is modeled by both phase-only oscillators and by van der Pol oscillators. We obtain and compare the existence, stability and bifurcation of the steady states in these two models. This work is motivated by application to the design of a machine which can make decisions by identifying a given initial condition with its associated steady state.     

Using artificial intelligence and computer technologies for developing treatment programs for complex immune diseases

This article deals with a methodology of using up-to-date mathematical and informational techniques to control the human immune system. Through the example of mathematically controlled HIV therapy, prospects are demonstrated for technologies that have never been used in medicine. The results indicate the possibility of significant advances in prolonging the life of HIV-infected people with the aid of mathematical technologies, which allow reducing the amount of chemotherapy.     

Stochastic dynamic lot-sizing problem using bi-level programming base on artificial intelligence techniques

Simulation is generally used to study non-deterministic problems in industry. When a simulation process finds the solution to an NP-hard problem, its efficiency is lowered, and computational costs increase. This paper proposes a stochastic dynamic lot-sizing problem with asymmetric deteriorating commodity, in which the optimal unit cost of material and unit holding cost would be determined. This problem covers a sub-problem of replenishment planning, which is NP-hard in the computational complexity theory. Therefore, this paper applies a decision system, based on an artificial neural network (ANN) and modified ant colony optimization (ACO) to solve this stochastic dynamic lot-sizing problem. In the methodology, ANN is used to learn the simulation results, followed by the application of a real-valued modified ACO algorithm to find the optimal decision variables. The test results show that the intelligent system is applicable to the proposed problem, and its performance is better than response surface methodology.     

Using artificial intelligence and visual techniques into preference disaggregation analysis: The MUDAS system

Multicriteria Disaggregation-Aggregation (D-A) approaches include two major issues: The first one is to assess the decision maker's preference model from his global preferences on a reference set of alternative actions. The second issue concerns the interactive character of the approach in order to aid the DM to understand his/her own way of preferring. This paper aims to present the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), an implementation of the extended UTA II method. Also, the contribution of the Artificial Intelligence and Visual techniques, embedded into the procedures of the MIIDAS system, is presented. Finally, a case study coming from a portfolio management selection problem is used to illustrate the system.     

Acting with systems intelligence: integrating complex responsive processes with the systems perspective

The systems thinking literature assumes that the concept of a system is useful in management and organizational research. Ralph D. Stacey and his collaborators, however, have questioned this. They have presented the theory of complex responsive processes (CRP) as an alternative to systems thinking. We argue that systems thinking and the CRP perspective are complementary. The CRP illuminates many of the micro-behavioural, local interaction and creativity-related organizational phenomena whereas the systems perspective is useful for other purposes. CRP misses the mark in its criticism of systems thinking. The insights of CRP should and could be incorporated, not switched, with the systems perspective. The systems intelligence perspective, proposed by Hämäläinen and Saarinen, provides a framework to accomplish that. By integrating systems thinking and the CRP model we hope to provide a platform from which it is possible to appreciate the relative merits of the two apparently conflicting strands of thought.