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.

/pdf/intelligent-agents-theory-and-practice-5dchj7p8pc.pdf

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)

https://arc.aiaa.org/doi/pdf/10.2514/6.2006-5977

MMOPS: Assessing the Impact of On-Board Autonomy for Deep Space Robotic Missions

The FP7-SPACE Project PRoViScout (Planetary Robotics Vision Scout) aimed to demonstrate the feasibility of vision-based autonomous sample identification & selection coupled with vision-based navigation for a long range planetary scouting/exploration mission along with the necessary robotic elements. After 2 years of development and integration, the project held a field trial campaign in the caldera of Tenerife in September 2012. This paper gives an overview of the trials and summarises its major findings in terms of equipment, software integration, trial's logistics & strategy, and the main trial results.

/pdf/the-proviscout-field-trials-tenerife-2012-integrated-testing-3a61tak2i5.pdf

The proviscout field trials tenerife 2012 - integrated testing of aerobot mapping, rover navigation and science assessment

The introduction of PDDL+ allowed more accurate representations of complex real-world problems of interest to the scientific community. However, PDDL+ problems are notoriously challenging to planners, requiring more advanced heuristics. We introduce the Temporal Pattern Database (TPDB), a new domain-independent heuristic technique designed for PDDL+ domains with mixed discrete/continuous behaviour, non-linear system dynamics, processes, and events. The pattern in the TPDB is obtained through an abstraction based on time and state discretisation. Our approach combines constraint relaxation and abstraction techniques, and uses solutions to the relaxed problem, as a guide to solving the concrete problem with a discretisation fine enough to satisfy the continuous model’s constraints.

PDDL+ Planning with Temporal Pattern Databases

Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present further developments of the work described in (Fox et al. 2006), where models of behaviours were learned as Hidden Markov Models. Execution of behaviours is monitored by tracking the most likely trajectory through such a learned model, while possible failures in execution are identified as deviations from common patterns of trajectories within the learned models. We present results for our experiments with a model learned for a robot behaviour.

/pdf/using-learned-action-models-in-execution-monitoring-3qp8i0i5le.pdf

Using learned action models in execution monitoring

Local search algorithms operate by making small changes to candidate solutions with the aim of reaching new and improved solutions The problem is that often the search will become trapped at sub optimal states from where there are no improving neighbours Much research has gone into creating schemes to avoid these local optima and various strategies exist mainly based around altering the acceptance function Another approach is Variable Neighbourhood Search which aims to bypass optima by linearly switching through multiple search neighbourhoods We propose a new method where the selection of neighbourhoods is dynamically decided dependant on the violations of the problem constraints, Constraint Directed Variable Neighbourhood Search We compared Constraint Directed Variable Neighbourhood Search to Variable Neighbourhood Search and show that the same search progress can be achieved whilst exploring only a fraction of the states

Derek Long

Papers

MMOPS: Assessing the Impact of On-Board Autonomy for Deep Space Robotic Missions

The proviscout field trials tenerife 2012 - integrated testing of aerobot mapping, rover navigation and science assessment

PDDL+ Planning with Temporal Pattern Databases

Using learned action models in execution monitoring

Constraint directed variable neighbourhood search