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)

Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policy-learning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea.

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Plan-based policy-learning for autonomous feature tracking

We report a pilot study on in situ analysis of backscatter data for intelligent control of a scientific instrument on an Autonomous Underwater Vehicle (AUV) carried out at the Monterey Bay Aquarium Research Institute (MBARI). The objective of the study is to investigate techniques which use machine intelligence to enable event-response scenarios. Specifically we analyse a set of techniques for automated sample acquisition in the water-column using an electro-mechanical "Gulper", designed at MBARI. This is a syringe-like sampling device, carried onboard an AUV. The techniques we use in this study are clustering algorithms, intended to identify the important distinguishing characteristics of bodies of points within a data sample. We demonstrate that the complementary features of two clustering approaches can offer robust identification of interesting features in the water-column, which, in turn, can support automatic event-response control in the use of the Gulper.

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In Situ Analysis for Intelligent Control

In most work in plan generation and execution the assumption has been made that the goals being addressed by the planning system (and executive) are imposed externally and that once a plan has been constructed to achieve these goals the activity of the planner can cease. Similarly, once the plan has been successfully executed and a state satisfying the externally imposed goals has been reached, it has been assumed that the planning and execution behaviours will suspend until a new goal set and consequent plan has been imposed. These assumptions do not hold for fully autonomous systems, which are capable of directing their own behaviour and prioritising their own goals. The problem we are most concerned with is determining how goals arise during the autonomous behaviour of a system. Recent work by Knight et al (Knight et al. 2001) and Chien et al (Chien et al. 2001) relaxed the above assumptions by introducing the idea of continuous planning in the Casper system. Casper is an architecture for an autonomous planning and control system intended for application in space missions involving onboard autonomy. In (Chien et al. 2001), the authors indicate that the system can respond to failures and opportunities during execution of a plan within a given horizon and, in principle, update its goal set in response to environmental factors. The RAX system (Jonsson et al. 2000) demonstrated the integration of planning, execution, failure diagnosis and subsequent plan repair. Our approach to autonomous planning and execution is to address the issues of goal generation and management according to the changing motivations of an autonomous system. Although in general an autonomous system will be created to carry out externally-imposed tasks, any extended period of autonomous behaviour will require a system to react to its environment and to be able to create its own goals. The MADbot project concerns the development of a motivated autonomous system, capable of generating its own goals in accordance with a system of drives and impulses (Coddington & Luck 2004) and monitoring its own behaviour in the execution of plans. Execution monitoring is done with respect to stochastic models of the actions that the robot can execute. At any point during the execution of its plan the robot can estimate its most likely state based on the appropriate model and the current observations of the system. If

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MADbot: a motivated and goal directed robot

This paper demonstrates that generating arguments in natural language requires planning at an abstract level, and that the appropriate abstraction cannot be captured by approaches based solely upon coherence relations. An abstraction based planning system is presented which employs operators motivated by empirical study and rhetorical maxims. These operators include a subset of traditional deductive rules of inference, argumentation theoretic rules of refutation, and inductive reasoning patterns. The paper presents a unified system in which the various argument forms are employed in generating rich, complex structures for persuasive text.

Generating the Structure of Argument

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning.

Derek Long

Papers

Plan-based policy-learning for autonomous feature tracking

In Situ Analysis for Intelligent Control

MADbot: a motivated and goal directed robot

Generating the Structure of Argument

Progress in AI Planning Research and Applications