An Introduction to MultiAgent Systems.

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Automated Planning: Theory and Practice.

Recent work has demonstrated that many social networks, and indeed many networks of other types also, have broad distributions of vertex degree. Here we show that this has a substantial impact on the shape of ego-centered networks, i.e., sets of network vertices that are within a given distance of a specified central vertex, the ego. This in turn affects concepts and methods based on ego-centered networks, such as snowball sampling and the "ripple effect". In particular, we argue that one's acquaintances, one's immediate neighbors in the acquaintance network, are far from being a random sample of the population, and that this biases the numbers of neighbors two and more steps away. We demonstrate this concept using data drawn from academic collaboration networks, for which, as we show, current simple theories for the typical size of ego-centered networks give numbers that differ greatly from those measured in reality. We present an improved theoretical model which gives significantly better results.

Ego-centered networks and the ripple effect

https://hal.archives-ouvertes.fr/hal-01137921/document

Deliberation for autonomous robots: A survey

https://www.cs.umd.edu/~nau/papers/ghallab2014actors.pdf

The actor's view of automated planning and acting: A position paper

One way to improve the robustness and flexibility of robot performance is to let the robot learn from its ex- periences. In this paper, we describe the architecture and knowledge-representation framework for a service robot being developed in the EU project RACE, and present examples illustrating how learning from experi- ences will be achieved. As a unique innovative feature, the framework combines memory records of low-level robot activities with ontology-based high-level seman- tic descriptions.

/pdf/an-ontology-based-multi-level-robot-architecture-for-540kptgrfe.pdf

An Ontology-based Multi-level Robot Architecture for Learning from Experiences

Plan-based robot control has to consider a multitude of aspects of tasks at once, such as task dependency, time, space, and resource usage. Hybrid planning is a strategy for treating them jointly. However, by incorporating all these aspects into a hybrid planner, its search space is huge by construction. This paper introduces the planner CHIMP, which is based on meta-CSP planning to represent the hybrid plan space and uses hierarchical planning as the strategy for cutting efficiently through this space. The paper makes two contributions: First, it describes how HTN planning is integrated into meta-CSP reasoning leading to a planner that can reason about different forms of knowledge and that is fast enough to be used on a robot. Second, it demonstrates CHIMP's task merging capabilities, i.e., the unification of different tasks from different plan parts, resulting in plans that are more efficient to execute. It also allows to merge new tasks online into a plan that is being executed. This is demonstrated on a PR2 robot.

Online task merging with a hierarchical hybrid task planner for mobile service robots

This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.

/pdf/the-race-project-robustness-by-autonomous-competence-1tppeejqkn.pdf

The RACE Project : Robustness by Autonomous Competence Enhancement

AI-based solutions for robot planning have so farfocused on very high-level abstractions of robot capabilitiesand of the environment in which they operate. However, tobe useful in a robotic context, the model provided to an AIplanner should afford both symbolic and metric constructs;its expressiveness should not hinder computational efﬁciency;and it should include causal, spatial, temporal and resourceaspects of the domain. We propose a planner grounded onwell-founded constraint-based calculi that adhere to theserequirements. A proof of completeness is provided, and theﬂexibility and portability of the approach is validated throughseveral experiments on real and simulated robot platforms.

More Knowledge on the Table:Planning with Space, Time and Resources for Robots

Deploying fleets of autonomous robots in real-world applications requires addressing three problems: motion planning, coordination, and control. Application-specific features of the environment and ...

https://research.birmingham.ac.uk/portal/files/76013637/A_loosely_coupled.pdf

Masoumeh Mansouri

Papers

An Ontology-based Multi-level Robot Architecture for Learning from Experiences

Online task merging with a hierarchical hybrid task planner for mobile service robots

The RACE Project : Robustness by Autonomous Competence Enhancement

More Knowledge on the Table:Planning with Space, Time and Resources for Robots

A Loosely-Coupled Approach for Multi-Robot Coordination, Motion Planning and Control