The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

Explanation in artificial intelligence: Insights from the social sciences

This chapter discusses leading problems linked to energy that the world is now confronting and to propose some ideas concerning possible solutions. Oil deserves special attention among all energy sources. Since the beginning of 1981, it has merely been continuing and enhancing the downward movement in consumption and prices caused by excessive rises, especially for light crudes such as those from Africa, and the slowing down of worldwide economic growth. Densely-populated oil-producing countries need to produce to live, to pay for their food and their equipment. If the economic growth of the industrialized countries were to be 4%, even if investment in the rational use of energy were pushed to the limit and the development of nonpetroleum energy sources were also pursued actively, it would be extremely difficult to prevent a sharp rise in prices. It is evident that it is absolutely necessary to pursue actively the development of coal, natural gas, and nuclear power if a physical shortage of energy is not to block economic growth.

World energy outlook

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

STAN is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. STAN competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, finding solutions fastest to many of the problems posed. Although the domain analysis techniques STAN exploits are an important factor in its overall performance, we believe that the speed at which STAN solved the competition problems is largely due to the implementation of its plan graph. The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the fix point. This paper describes the implementation of STAN's plan graph and provides experimental results which demonstrate the circumstances under which advantages can be obtained from using this implementation.

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Efficient implementation of the plan graph in STAN

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Efficient Implementation of the Plan Graph in STAN

Many complex domains and even larger problems in simple domains remain challenging in spite of the recent progress in planning. Besides developing and improving planning technologies, re-engineering a domain by utilising acquired knowledge opens up a potential avenue for further research. Moreover, macro-actions, when added to the domain as additional actions, provide a promising means by which to convey such knowledge. A macro-action, or macro in short, is a group of actions selected for application as a single choice. Most existing work on macros exploits properties explicitly specific to the planners or the domains. However, such properties are not likely to be common with arbitrary planners or domains. Therefore, a macro learning method that does not exploit any structural knowledge about planners or domains explicitly is of immense interest. This paper presents an offline macro learning method that works with arbitrarily chosen planners and domains. Given a planner, a domain, and a number of example problems, the learning method generates macros from plans of some of the given problems under the guidance of a genetic algorithm. It represents macros like regular actions, evaluates them individually by solving the remaining given problems, and suggests individual macros that are to be added to the domain permanently. Genetic algorithms are automatic learning methods that can capture inherent features of a system using no explicit knowledge about it. Our method thus does not strive to discover or utilise any structural properties specific to a planner or a domain.

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Learning macro-actions for arbitrary planners and domains

The notion of plan qualityin automated planning is a practically very important issue. In many real-world planning domains, we have to address problems with a large set of solutions, or with a set of goals that cannot all be achieved. In these problems, it is important to generate plans of good or optimal quality achieving all problem goals (if possible) or some subset of them. In the previous International planning competitions, the plan generation CPU-time played a central role in the evaluation of the competing planners. In the fifth International planning competition (IPC-5), while considering the CPU-time, we would like to give greater emphasis to the importance of plan quality. The versions of PDDL used in the previous two competitions (PDDL2.1 and PDDL2.2) allow us to express some criteria for plan quality, such as the number of plan actions or parallel steps, and relatively complex plan metrics involving plan makespan and numerical quantities. These are powerful and expressive in domains that include metric fluents, but plan quality can still only be measured by plan size in the case of propositional planning. We believe that these criteria are insufficient, and we propose to extend PDDL with new constructs increasing its expressive power about the plan quality specification.

Preferences and soft constraints in PDDL3

Graphplan (Blum & Furst 1995) has proved a popular and successful basis for a succession of extensions. An extension to handle temporal planning is a natural one to consider, because of the seductively time-like structure of the layers in the plan graph. TGP (Smith & Weld 1999) and TPSys (Garrido, Onaindia, & Barber 2001; Garrido, Fox, & Long 2002) are both examples of temporal planners that have exploited the Graphplan foundation. However, both of these systems (including both versions of TPSys) exploit the graph to represent a uniform flow of time. In this paper we describe an alternative approach, in which the graph is used to represent the purely logical structuring of the plan, with temporal constraints being managed separately (although not independently). The approach uses a linear constraint solver to ensure that temporal durations are correctly respected. The resulting planner offers an interesting alternative to the other approaches, offering an important extension in expressive power.

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Derek Long

Papers

Efficient implementation of the plan graph in STAN

Efficient Implementation of the Plan Graph in STAN

Learning macro-actions for arbitrary planners and domains

Preferences and soft constraints in PDDL3

Exploiting a Graphplan framework in temporal planning