OR and AI approaches to decision support systems

First the concept of a decision support system (dss) is described. Then the first approach, in which the development of a network of models forms the kernel, is given. An example illustrates this approach. Then, architectures of decision support systems according to the first approach are given. Afterwards a second approach is considered, in which an (abstract) dss machine is described that can be tuned to specific decision situations. Finally the applicability of this approach is illustrated with an example.     

Creativity: a survey of AI approaches

In this paper we critically survey the AI programs that have been developed to exhibit some aspect of creative behaviour. We describe five necessary characteristics of models of creativity, and we apply these characteristics to help assess the programs surveyed. These characteristic features also provide a basis for a new theory of creative behavior: an emergent memory model. The survey is concluded with an assessment of an implementation of this latest theory.

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Numerical and symbolic approaches to uncertainty management in AI

Dealing with uncertainty is part of most intelligent behaviour and therefore techniques for managing uncertainty are a critical step in producing intelligent behaviour in machines. This paper discusses the concept of uncertainty and approaches that have been devised for its management in AI and expert systems. These are classified as quantitative (numeric) (Bayesian methods, Mycin's Certainty Factor model, the Dempster-Shafer theory of evidence and Fuzzy Set theory) or symbolic techniques (Nonmonotonic/Default Logics, Cohen's theory of Endorsements, and Fox's semantic approach). Each is discussed, illustrated, and assessed in relation to various criteria which illustrate the relative advantages and disadvantages of each technique. The discussion summarizes some of the criteria relevant to selecting the most appropriate uncertainty management technique for a particular application, emphasizes the differing functionality of the approaches, and outlines directions for future research. This includes combining qualitative and quantitative representations of information within the same application to facilitate different kinds of uncertainty management and functionality.     

AI approaches to identification and control of total plant production systems

Recent progress in intelligent control techniques has enabled complex systems such as cultivation and fruit-storage processes to be dealt with. This paper presents the application of a hierarchical intelligent control system, which consists of an expert system and an optimizer based on neural networks and genetic algorithms, for optimizing a total plant production process. Environmental factors in the cultivation and storage processes are optimally controlled, based on the physiological status of the plant (or fruit). The expert system determines suitable environmental setpoints throughout growth, and the optimizer determines optimal environmental setpoints during important growth stages and during storage, based on plant responses. In the optimizer, neural networks were used for the identification of plant responses to environmental factors, and genetic algorithms were used to search for the optimal environmental setpoints through the simulation of the identified models. Optimal setpoints of the nutrient concentration in hydroponic tomato cultivation and optimal setpoints of the temperature during tomato storage were determined using this control technique.     

结合AI规划和工作流的动态服务组合框架研究

动态服务组合是构建面向服务、松耦合、集成化的应用系统的主要途径.现行的动态服务组合方法中,工业界倾向于采用BPEL建模服务流程,而语义网研究团体则吸取AI规划的思想以实现Web服务组合,以上两种方法都不同程度地存在各自的不足.在对上述方法进行对比分析的基础上,提出了一种结合AI规划和工作流技术的动态服务组合方法.实践表明,这种方法能有效克服采用单个方法所造成的不足,并可实现从需求到部署的全过程.     

Complementary menus: Combining adaptable and adaptive approaches for menu interface

This study proposes a method of coupling adaptable and adaptive approaches to the design of menus. The proposed complementary menu types incorporate both adaptability and adaptivity by dividing and allocating menu adaptation roles to the user and the system. Four different types of interface adaptation (i.e., adaptable with/without system support and adaptive with/without user control) were defined. They were implemented in a hypothetical prototype mobile phone via a hotlist (an additional collection of quickly accessible items). A controlled lab experiment was conducted to compare the menu types and investigate the effects of the system support in the adaptable menus and the user control in the adaptive menus. Twenty subjects participated in the experiment and performed menu selection tasks. Both performance and user satisfaction measures were collected. The results showed that adaptable and adaptive menus were superior to the traditional one in terms of both performance and user satisfaction. Providing system support to the adaptable menu not only increased the users’ perception of the efficiency of selection, but also reduced the menu adaptation time. Important implications for the design of menus are described and valuable insights into the menu interface adaptation were gained from the quantitative and qualitative analyses of the experimental results.

Relevance to industry

The evaluation experiment conducted in this study may provide valuable information to designers of adaptive or adaptable menus. Adding system support to adaptable menu would be an attractive option to consider. Also, the results of a user survey provide useful information to the practitioners in mobile phone industry on the features users accessed most frequently.     

Game AI is Dead. Long Live Game AI!

This paper discusses how the recent advances in game AI technology can help benefit industry and the academia. Some of these advances include large-scale automated art and content generation, automated storytelling, goal-driven virtual actors, and the ability to adapt to the player's preferences and mental state     

Combining univariate approaches for ensemble change detection in multivariate data

Detecting change in multivariate data is a challenging problem, especially when class labels are not available. There is a large body of research on univariate change detection, notably in control charts developed originally for engineering applications. We evaluate univariate change detection approaches —including those in the MOA framework — built into ensembles where each member observes a feature in the input space of an unsupervised change detection problem. We present a comparison between the ensemble combinations and three established ‘pure’ multivariate approaches over 96 data sets, and a case study on the KDD Cup 1999 network intrusion detection dataset. We found that ensemble combination of univariate methods consistently outperformed multivariate methods on the four experimental metrics.     

Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results

This paper summarizes the main existing approaches to propagate resource constraints in Constraint-Based scheduling and identifies some of their limitations for using them in an integrated planning and scheduling framework. We then describe two new algorithms to propagate resource constraints on discrete resources and reservoirs. Unlike most of the classical work in scheduling, our algorithms focus on the precedence relations between activities rather than on their absolute position in time. They are efficient even when the set of activities is not completely defined and when the time window of activities is large. These features explain why our algorithms are particularly suited for integrated planning and scheduling approaches. All our algorithms are illustrated with examples. Encouraging preliminary results are reported on pure scheduling problems as well as some possible extensions of our framework.     

Combining uncertainty and imprecision in models of medical diagnosis

The paper presents a unified fuzzy-probabilistic framework for modeling processes of medical diagnosis. The two basic concepts of the Dempster–Shafer theory, i.e. focal elements and a basic probability assignment, correspond to disease symptoms and the significance of an individual symptom in the diagnosis, respectively. The belief computation is related to diagnostic inference. The final conclusion of the inference is the diagnosis with the greatest belief value. Fuzzy sets are used to describe focal elements. It is shown how their membership functions and basic probability assignments are estimated on the basis of experimental data. The interpretation of focal elements as fuzzy sets along with individual consideration of evidence imprecision and uncertainty of diagnosis are the essential new aspects of the presented method. Experimental studies have demonstrated the superiority of the proposed approach over some other modeling alternatives.     

Associating domain-dependent knowledge and Monte Carlo approaches within a Go program

This paper underlines the association of two computer go approaches, a domain-dependent knowledge approach and Monte Carlo. First, the strengthes and weaknesses of the two existing approaches are related. Then, the association is described in two steps. A first step consists in using domain-dependent knowledge within the random games enabling the program to compute evaluations that are more significant than before. A second step simply lies in pre-processing the Monte Carlo process with a knowledge-based move generator in order to speed up the program and to eliminate tactically bad moves. We set up experiments demonstrating the relevance of this association, used by Indigo at the 8th computer olympiad as well.     

Combining ontology reductions with new approaches to automated abstract planning of Planics

The paper deals with the first phase of Web Service Composition – the abstract planning problem, performed by the tool Planics. We discuss three algorithms based on multisets of service types being a concise representation of abstract plans. The first algorithm reduces the abstract planning problem to a satisfiability problem for an SMT-solver (SMT stands for the Satisfiability Modulo Theories), the second one follows a GA-based approach (GA stands for a genetic algorithm), while the third hybrid algorithm combines the two above. All the algorithms are applied to ontologies, which are efficiently reduced using a graph database approach. The paper presents theoretical aspects of the framework together with many examples, and the extensive experimental results of the four algorithms followed by their analysis and comparison with each other and other approaches.     

AI and similarity

As AI moves into the second half of its first century, we certainly have much to cheer about. For AI to become truly robust, we must further our understanding of similarity-driven reasoning, analogy, learning, and explanation. In this article, the author presents some suggested research directions.     

Professionalism and AI

The time has come for all those working in AI to take the issue of professionalism seriously. Professional standards will be difficult to establish in AI. However, there will be pressure from various directions to produce a code or codes which will demonstrate that work is being done responsibly. Such codes will be largely worthless unless they are produced by people actually working at the sharp end of AI.