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.     

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     

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.     

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.     

Combining expert knowledge and data mining in a medical diagnosis domain

The medical diagnosis system described here uses underlying knowledge in the isokinetic domain, obtained by combining the expertise of a physician specialised in isokinetic techniques and data mining techniques applied to a set of existing data. An isokinetic machine is basically a physical support on which patients exercise one of their joints, in this case the knee, according to different ranges of movement and at a constant speed. The data on muscle strength supplied by the machine are processed by an expert system that has built-in knowledge elicited from an expert in isokinetics. It cleans and pre-processes the data and conducts an intelligent analysis of the parameters and morphology of the isokinetic curves. Data mining methods based on the discovery of sequential patterns in time series and the fast Fourier transform, which identifies similarities and differences among exercises, were applied to the processed information to characterise injuries and discover reference patterns specific to populations. The results obtained were applied in two environments: one for the blind and another for elite athletes.     

The role of AI technology in prediction,diagnosis and treatment of colorectal cancer

Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.

     

Combining automatic acquisition of knowledge with machine learning approaches for multilingual temporal recognition and normalization

This paper presents an improvement in the temporal expression (TE) recognition phase of a knowledge based system at a multilingual level. For this purpose, the combination of different approaches applied to the recognition of temporal expressions are studied. In this work, for the recognition task, a knowledge based system that recognizes temporal expressions and had been automatically extended to other languages (TERSEO system) was combined with a system that recognizes temporal expressions using machine learning techniques. In particular, two different techniques were applied: maximum entropy model (ME) and hidden Markov model (HMM), using two different types of tagging of the training corpus: (1) BIO model tagging of literal temporal expressions and (2) BIO model tagging of simple patterns of temporal expressions. Each system was first evaluated independently and then combined in order to: (a) analyze if the combination gives better results without increasing the number of erroneous expressions in the same percentage and (b) decide which machine learning approach performs this task better. When the TERSEO system is combined with the maximum entropy approach the best results for F-measure (89%) are obtained, improving TERSEO recognition by 4.5 points and ME recognition by 7.     

Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings

This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology.