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 longstanding debate in political economy about the feasibility of socialism, the Austrian School of Economists have argued that markets are an indispensable means of evaluating goods, hence a prerequisite for productive efficiency. Socialist models for non-market economic calculation have been strongly influenced by the equilibrium model of neoclassical economics. The Austrians contend that these models overlook the essence of the calculation problem by assuming the availability of knowledge that can be acquired only through the market process itself. But the debate in political economy has not yet considered the recent emergence of agent-based systems and their applications to resource allocation problems. Agent-based simulations of market exchange offer a promising approach to fulfilling the dynamic functions of knowledge encapsulation and discovery that the Austrians show to be performed by markets. Further research is needed in order to develop an agent-based approach to the calculation problem, as it is formulated by the Austrians. Given that the macro-level objectives of agent-based systems can be easily engineered, they could even become a desirable alternative to the real markets that the Austrians favour.

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.

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.

In this paper, we present a model of the spread of the COVID-19 pandemic simulated by a multi-agent system (MAS) based on demographic data and medical knowledge. Demographic data are linked to the distribution of the population according to age and to an index of socioeconomic fragility with regard to the elderly. Medical knowledge are related to two risk factors: age and obesity. The contributions of this approach are as follows. Firstly, the two aggravating risk factors are introduced into the MAS using fuzzy sets. Secondly, the worsening of disease caused by these risk factors is modeled by fuzzy aggregation operators. The appearance of virus variants is also introduced into the simulation through a simplified modeling of their contagiousness. Using real data from inhabitants of an island in the Antilles (Guadeloupe, FWI), we model the rate of the population at risk which could be critical cases, if neither social distancing nor barrier gestures are respected by the entire population. The results show that hospital capacities are exceeded. The results show that hospital capacities are exceeded. The socioeconomic fragility index is used to assess mortality and also shows that the number of deaths can be significant.

Few would disagree with the assertion that safe engineering starts from the early stages of system design and should be maintained throughout the lifecycle. Different engineering domains have developed, mostly informal, frameworks with which they hope to promote this attitude. An interesting question for the KBS community is whether some of our methods for knowledge representation and reasoning can be used to assist in understanding, representing and interpreting such frameworks. This paper concentrates on what is (arguably) the area of greatest concern: relating system requirements to high level design. We highlight what appear to be the major difficulties which face us in this area, using examples from systems which have been built to tackle them.

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.

Ensuring reliability and enhancing quality of Knowledge Based Systems (KBS) are critical factors for their successful deployment in real-world applications. This is a broad task involving both methodological and formal approaches for designing rigorous Validation, Verification and Testing (VVT) methods and tools. Some of these can be adapted from conventional software engineering, while others rely on specific aspects of KBS.

This paper introduces Rudder, a peer-to-peer agent framework for supporting autonomic applications in decentralized distributed environments. The framework provides agents to discover, select, and compose elements, and defines agent interaction and negotiation protocols to enable appropriate application behaviors to be negotiated and enacted dynamically. The implementations of these protocols as well as agent coordination and negotiation activities are supported by Comet, a scalable decentralized coordination substrate. The operation and experimental evaluation of Rudder is presented.

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.

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.

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.

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.