Inductive Logic Programming (ILP) is an emerging research area at the intersection of machine learning, logic programming and software engineering. The first workshop on this topic was held in 1991 in Portugal (Muggleton, 1991). Subsequently, there was a workshop tied to the Future Generation Computer System Conference in Japan in 1992, and a third one in Bled, Slovenia, in April 1993 (Muggleton, 1993). Ideas related to ILP are also appearing in major AI and machine learning conferences and journals. Although European-based and mainly sponsored by ESPRIT, ILP aims at becoming equally represented elsewhere; for example, among researchers in America who are investigating relational learning and first order theory revision (see, for example, the papers in Birnbaum and Collins, 1991) and within the computational learning theory community. This year's IJCAI workshop on ILP is a first step in this direction, and includes recent work with a broader range of perspectives and techniques.

Artificial intelligence research in temporal reasoning focuses on designing automated solutions to complex problems in computation involving time. TIME-97, the 4th International Workshop on Temporal Representation and Reasoning, held in Daytona Beach, Florida — like the three workshops that preceded it — had the objective of creating an international forum for the exchange of information among the many researchers and knowledge engineers who are developing and applying techniques in temporal reasoning.

In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the XR4DRAMA framework. The KG of the XR4DRAMA framework can represent data for media preparation and disaster management. More specifically, the KG of the XR4DRAMA framework can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). In addition, we provide a mechanism that allows Points of Interest (POI) to be created or updated based on videos, photos, and text messages sent by users. For improved disaster management and media coverage of a location, POI serve as markers to journalists and first responders. A task creation mechanism is also provided for the disaster management scenario with the XR4DRAMA framework, which indicates to first responders and citizens what tasks need to be performed in case of an emergency. Finally, the XR4DRAMA framework has a danger zone creation mechanism. Danger zones are regions in a map that are considered as dangerous for citizens and first responders during a disaster management scenario and are annotated by a severity score. The last two mechanisms are based on a Decision Support System (DSS).

The first conference on Uncertainty in Artificial Intelligence was held in 1985 by a group of people who felt that their views on the use of probability theory were not receiving a fair hearing from the rest of the Al community. At the time, mainstream opinion held that computational complexity of, and the amount of data required by, probabilistic methods made them inappropriate for realistic applications. As a result, those who claimed that probability theory was an adequate, if not the only adequate, method of handling uncertainty received a somewhat frosty reception.

For the first time, post-conference workshops were organised for the International Conference on Deductive and Object-Oriented Databases (DOOD). There were two workshops focusing on knowledge discovery and temporal reasoning. This report is dedicated to one dealing with temporal reasoning.

Reinforcement Learning (RL) methods often rely on gradient estimates to learn an optimal policy for control problems. These expensive computations result in long training times, a poor rate of convergence, and sample inefficiency when applied to real-world problems with a large state and action space. Evolutionary Computation (EC)-based techniques offer a gradient-free apparatus to train a deep neural network for RL problems. In this work, we leverage the benefits of EC and propose a novel variant of genetic algorithm called SP-GA which utilizes a species-inspired weight initialization strategy and trains a population of deep neural networks, each estimating the Q-function for the RL problem. Efficient encoding of a neural network that utilizes less memory is also proposed which provides an intuitive mechanism to apply Gaussian mutations and single-point crossover. The results on Atari 2600 games outline comparable performance with gradient-based algorithms like Deep Q-Network (DQN), Asynchronous Advantage Actor Critic (A3C), and gradient-free algorithms like Evolution Strategy (ES) and simple Genetic Algorithm (GA) while requiring far fewer hyperparameters to train. The algorithm also improved certain Key Performance Indicators (KPIs) when applied to a Remote Electrical Tilt (RET) optimization task in the telecommunication domain.

The expert systems business has been through a terrible period in 1987–88. In the USA, the “Gang of Four”—the new venture software companies which once led the industry—have all run into losses and cut back staff more or less severely. Symbolics, whose high priced Lisp workstations have made it the biggest single company in artificial intelligence (AI), has hit an equally dramatic reversal of fortunes.

High utility itemsets mining (HUIM) is an important sub-field of frequent itemset mining (FIM). Recently, HUIM has received much attention in the field of data mining. High utility itemsets (HUIs) have proven to be quite useful in marketing, retail marketing, cross-marketing, and e-commerce. Traditional HUIM approaches suffer from a drawback as they need a user-defined minimum utility ($ min\_util $) threshold. It is not easy for the users to set the appropriate $ min\_util $ threshold to find actionable HUIs. To target this drawback, top-k HUIM has been introduced. top-k HUIM is more suitable for supermarket managers and retailers to prepare appropriate strategies to generate higher profit. In this paper, we provide an in-depth survey of the current status of top-k HUIM approaches. The paper presents the task of top-k HUIM and its relevant definitions. It reviews the top-k HUIM approaches and presents their advantages and disadvantages. The paper also discusses the important strategies of the top-k HUIM, their variations, and research opportunities. The paper provides a detailed summary, analysis, and future directions of the top-k HUIM field.

Reusing requirements improves product quality and the productivity of the development process. This paper investigates how the development of new requirements can be made more productive through reuse of the experience gained on similar requirements. This can be facilitated using a component-based reuse approach supported by a framework. Therefore, the central challenge for this research work is double: (1) to define a new concept for the requirement component as the combination of two types of knowledge: reusable knowledge and knowledge of reuse; (2) to define AFR (Analysis For Reuse) that represents the capitalization process by opposition to Analysis By Reuse that represents the process of reuse itself. Finally, we provide a case study related to the requirements of a hotel system to explain how the first process works. Through this approach, we use a framework ‘Requirements Repository Framework (R2F)’ in order to capitalize existing requirements within a repository for future reuse. We present the results of an experiment with three second-level student sections that used R2F in their IS240 course project; as well as with four student groups that used R2F in their respective senior projects. The objective of this experiment is to measure the usability of the proposed AFR process.

Production systems have successfully made the transition from a trendy research idea to a routinely used programming paradigm. An important cause of this transition has been the several orders of magnitude speedup in program execution achieved in the past few years by the combination of better match algorithms, efficient compilation techniques and faster hardware platforms.

Most of the current top-performing planners are sequential planners that only handle total-order plans. Although this is a computationally efficient approach, the management of total-order plans restrict the choices of reasoning and thus the generation of flexible plans. In this paper, we present FLAP2, a forward-chaining planner that follows the principles of the classical POCL (Partial-Order Causal-Link Planning) paradigm. Working with partial-order plans allows FLAP2 to easily manage the parallelism of the plans, which brings several advantages: more flexible executions, shorter plan durations (makespan) and an easy adaptation to support new features like temporal or multi-agent planning. However, one of the limitations of POCL planners is that they require far more computational effort to deal with the interactions that arise among actions. FLAP2 minimizes this overhead by applying several techniques that improve its performance: the combination of different state-based heuristics and the use of parallel processes to diversify the search in different directions when a plateau is found. To evaluate the performance of FLAP2, we have made a comparison with four state-of-the-art planners: SGPlan, YAHSP2, Temporal Fast Downward and OPTIC. Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks.

Interest in agent-oriented technology continues to grow, both at a theoretical level and a practical level, with the UK maintaining a strong representation in the area in both academic institutions and commercial organizations. In December 1997, the Second UK Workshop on Foundations of Multi-Agent Systems (FoMAS'97), held at the University of Warwick, built on the success of FoMAS'96 a year earlier (Luck, 1997; Doran et al., 1997; d'Inverno et al., 1997; Fisher et al., 1997) in seeking to provide a forum for academics and industrialists within the UK to present and discuss current directions in research and applications development. Supported by the EPSRC and HP Labs, FoMAS'97 was expanded to two days with invited presentations from Professor Ken Binmore of UCL, and Professor Cristiano Castelfranchi of the Italian National Research Council's Institute of Psychology, in addition to paper presentations and panel discussions. The aim again was to provide an opportunity for promoting and supporting activity in the research and development of multi-agent systems.