Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

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

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. In this article, we present a survey of what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. The article also includes a short review of current and potential applications of agent technology, and closes with a glossary of key terms, an annotated list of systems, and a detailed bibliography. Pointers to further reading are provided throughout.

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Agent theories, architectures, and languages: a survey

1. Cognitive Architecture 2. The Modular Organization of the Mind 3. Human Associative Memory 4. The Adaptive Control of Thought 5. What Does It Take to Be Human? Lessons From High School Algebra 6. How Can the Human Mind Occur?

How Can the Human Mind Occur in the Physical Universe

A model of purely reactive planning is proposed based on the concept of reactive action packages. A reactive action package, or RAP, can be thought of as an independent entity pursuing some goal in competition with many others at execution time. The RAP processing algorithm addresses the problems of execution monitoring and replanning in uncertain domains with a single, uniform representation and control structure. Use of the RAP model as a basis for adaptive strategic planning is also discussed.

An investigation into reactive planning in complex domains

This paper describes an extension to the RAP system task-net semantics and representation language to enable the effective control of continuous processes. The representation addresses the problems of synchronizing plan expansion with events in the world, coping with multiple, non-deterministic task outcomes, and the description of a simple form of clean-up task.

It is also pointed out that success and failure need no special place in a task network representation. Success and failure are really messages about the execution system's knowledge and do not explicitly define that system's flow of control.

/pdf/task-networks-for-controlling-continuous-processes-54cymlqg79.pdf

Task networks for controlling continuous processes

This paper is about the interface between continuous and symbolic robot control. We advocate describing continuous actions and their related sensing strategies as situation specific activities , which can be manipulated by a symbolic reactive planner. The approach addresses the issues involved in turning symbolic actions into continuous activities, and using task specific sensing routines to support those activities. Situation specific activities help preserve the convenient fiction of “primitive actions” for use in planning without requiring that they all be programmed into the control system in advance. We demonstrate the utility of this architecture with an object tracking example. A control system is presented that can be reconfigured by a the RAP reactive executor to achieve different tasks. We show how this system allows us to build interchangeable tracking activities that use different sensing/action feedback loops in different situations.

Building symbolic primitives with continuous control routines

Vision systems that have successfully supported nontrivial tasks have invariably taken advantage of constraints derived from the task and environment to increase reliability and lower the complexity of perception. We propose that it is possible to build a general purpose vision system, that is, one that can support a wide variety of tasks, and take advantage of such constraints. The central idea within our proposed architecture is the reactive skill. Skills are concurrent control routines assembled at run time using instructions from a symbolic execution system. Visual modules are used as resources in the construction of these skills. Skills control the agent as continuous feedback loops but are constructed using discrete, symbolic instructions. The key to general-purpose vision is the ability to parametrize the primitive elements of the vision system and to compose visual and control routines in a variety of ways. We demonstrate the architecture in the context of an implemented example task of a robot collecting trash off a floor and depositing it in a garbage can.

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An architecture for vision and action

The RAP reactive plan execution system is specifically designed to accept plans and goals at a high level of abstraction and expand them into detailed actions at run time. One of the key features of the RAP system is the use of hierarchical expansion methods that allow different paths of execution for the same goals in different situations. Another central reason for the hierarchy is to create modular expansion methods that can be used in the execution of many different tasks. However, experience using the RAP system to control the University of Chicago robot Chip in the 1995 IJCAI robot competition has shown that there are difficult trade-offs between modularity and correctness in a predefined plan hierarchy.

This paper describes the RAP hierarchies used to control the robot while cleaning up a small office space and discusses some of the issues raised in writing these RAPs to be useful for other tasks as well. In particular, realistic reactive plans must support concurrency that crosses simple modular decomposition boundaries and efficient strategies for carrying out tasks that depend on active sensing require advanced knowledge of sensing requirements.

/pdf/modularity-issues-in-reactive-planning-2g1rrenkp5.pdf

R. James Firby

Papers

An investigation into reactive planning in complex domains

Task networks for controlling continuous processes

Building symbolic primitives with continuous control routines

An architecture for vision and action

Modularity issues in reactive planning