Subsumption architecture

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Subsumption architecture is a reactive robotic architecture heavily associated with behavior-based robotics which was very popular in the 1980s and 90s. The term was introduced by Rodney Brooks and colleagues in 1986. [1] [2] [3] Subsumption has been widely influential in autonomous robotics and elsewhere in real-time AI.

Behavior-based robotics (BBR) or behavioral robotics is an approach in robotics that focuses on robots that are able to exhibit complex-appearing behaviors despite little internal variable state to model its immediate environment, mostly gradually correcting its actions via sensory-motor links.

Rodney Brooks Australian Roboticist

Rodney Allen Brooks is an Australian roboticist, Fellow of the Australian Academy of Science, author, and robotics entrepreneur, most known for popularizing the actionist approach to robotics. He was a Panasonic Professor of Robotics at the Massachusetts Institute of Technology and former director of the MIT Computer Science and Artificial Intelligence Laboratory. He is a founder and former Chief Technical Officer of iRobot and co-Founder, Chairman and Chief Technical Officer of Rethink Robotics. Outside the scientific community Brooks is also known for his appearance in a film featuring him and his work, Fast, Cheap & Out of Control.

In computer science, real-time computing (RTC), or reactive computing describes hardware and software systems subject to a "real-time constraint", for example from event to system response. Real-time programs must guarantee response within specified time constraints, often referred to as "deadlines". The correctness of these types of systems depends on their temporal aspects as well as their functional aspects. Real-time responses are often understood to be in the order of milliseconds, and sometimes microseconds. A system not specified as operating in real time cannot usually guarantee a response within any timeframe, although typical or expected response times may be given.

Contents

Overview

Subsumption architecture is a control architecture that was proposed in opposition to traditional AI, or GOFAI. Instead of guiding behavior by symbolic mental representations of the world, subsumption architecture couples sensory information to action selection in an intimate and bottom-up fashion. [4] :130

A mental representation, in philosophy of mind, cognitive psychology, neuroscience, and cognitive science, is a hypothetical internal cognitive symbol that represents external reality, or else a mental process that makes use of such a symbol: "a formal system for making explicit certain entities or types of information, together with a specification of how the system does this".

Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behavior.

Top-down and bottom-up are both strategies of information processing and knowledge ordering, used in a variety of fields including software, humanistic and scientific theories, and management and organization. In practice, they can be seen as a style of thinking, teaching, or leadership.

It does this by decomposing the complete behavior into sub-behaviors. These sub-behaviors are organized into a hierarchy of layers. Each layer implements a particular level of behavioral competence, and higher levels are able to subsume lower levels (= integrate/combine lower levels to a more comprehensive whole) in order to create viable behavior. For example, a robot's lowest layer could be "avoid an object". The second layer would be "wander around", which runs beneath the third layer "explore the world". Because a robot must have the ability to "avoid objects" in order to "wander around" effectively, the subsumption architecture creates a system in which the higher layers utilize the lower-level competencies. The layers, which all receive sensor-information, work in parallel and generate outputs. These outputs can be commands to actuators, or signals that suppress or inhibit other layers. [5] :8–12;15–16

Goal

Subsumption architecture attacks the problem of intelligence from a significantly different perspective than traditional AI. Disappointed with the performance of Shakey the robot and similar conscious mind representation-inspired projects, Rodney Brooks started creating robots based on a different notion of intelligence, resembling unconscious mind processes. Instead of modelling aspects of human intelligence via symbol manipulation, this approach is aimed at real-time interaction and viable responses to a dynamic lab or office environment. [4] :130–131

Shakey the robot General-purpose mobile robot

Shakey the Robot was the first general-purpose mobile robot to be able to reason about its own actions. While other robots would have to be instructed on each individual step of completing a larger task, Shakey could analyze commands and break them down into basic chunks by itself.

The goal was informed by four key ideas:

In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if:

In artificial intelligence, an embodied agent, also sometimes referred to as an interface agent, is an intelligent agent that interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are also called embodied agents, although they have only virtual, not physical, embodiment. A branch of artificial intelligence focuses on empowering such agents to interact autonomously with human beings and the environment. Mobile robots are one example of physically embodied agents; Ananova and Microsoft Agent are examples of graphically embodied agents. Embodied conversational agents are embodied agents that are capable of engaging in conversation with one another and with humans employing the same verbal and nonverbal means that humans do.

Control system system to control other devices using control loops

A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large Industrial control systems which are used for controlling processes or machines.

The ideas outlined above are still a part of an ongoing debate regarding the nature of intelligence and how the progress of robotics and AI should be fostered.

Layers and augmented finite-state machines

Each layer is made up by a set of processors that are augmented finite-state machines (AFSM), the augmentation being added instance variables to hold programmable data-structures. A layer is a module and is responsible for a single behavioral goal, such as "wander around." There is no central control within or between these behavioral modules. All AFSMs continuously and asynchronously receive input from the relevant sensors and send output to actuators (or other AFSMs). Input signals that are not read by the time a new one is delivered end up getting discarded. These discarded signals are common, and is useful for performance because it allows the system to work in real time by dealing with the most immediate information.

Finite-state machine Mathematical model of computation

A finite-state machine (FSM) or finite-state automaton, finite automaton, or simply a state machine, is a mathematical model of computation. It is an abstract machine that can be in exactly one of a finite number of states at any given time. The FSM can change from one state to another in response to some external inputs; the change from one state to another is called a transition. An FSM is defined by a list of its states, its initial state, and the conditions for each transition. Finite state machines are of two types – deterministic finite state machines and non-deterministic finite state machines. A deterministic finite-state machine can be constructed equivalent to any non-deterministic one.

In object-oriented programming with classes, an instance variable is a variable defined in a class, for which each instantiated object of the class has a separate copy, or instance. An instance variable is similar to a class variable. An instance variable is a variable which is declared in a class but outside the constructor and the method/function. Instance variables are created when an object is instantiated, and are accessible to all the methods, the constructor and block in the class. Access modifiers can be given to the instance variable.

Modular programming is a software design technique that emphasizes separating the functionality of a program into independent, interchangeable modules, such that each contains everything necessary to execute only one aspect of the desired functionality.

Because there is no central control, AFSMs communicate with each other via inhibition and suppression signals. Inhibition signals block signals from reaching actuators or AFSMs, and suppression signals blocks or replaces the inputs to layers or their AFSMs. This system of AFSM communication is how higher layers subsume lower ones (see figure 1), as well as how the architecture deals with priority and action selection arbitration in general. [5] :12–16

Figure 1: Abstract representation of subsumption architecture, with the higher level layers subsuming the roles of lower level layers when the sensory information determines it. Subsumption Architecture Abstract Diagram.png
Figure 1: Abstract representation of subsumption architecture, with the higher level layers subsuming the roles of lower level layers when the sensory information determines it.

The development of layers follows an intuitive progression. First the lowest layer is created, tested, and debugged. Once that lowest level is running, one creates and attaches the second layer with the proper suppression and inhibition connections to the first layer. After testing and debugging the combined behavior, this process can be repeated for (theoretically) any number of behavioral modules. [5] :16–20

Robots

The following is a small list of robots that utilize the subsumption architecture.

The above are described in detail along with other robots in Elephants Don't Play Chess. [6]

Strengths and weaknesses

The main advantages of the architecture are:

The main disadvantages of the architecture are:

When subsumption architecture was developed, the novel setup and approach of subsumption architecture allowed it to be successful in many important domains where traditional AI had failed, namely real-time interaction with a dynamic environment. The lack of large memory storage, symbolic representations, and central control, however, places it at a disadvantage at learning complex actions, in-depth mapping, and understanding language.

See also

Notes

  1. Brooks, R. (1986). "A robust layered control system for a mobile robot". IEEE Journal of Robotics and Automation. 2 (1): 14–23. doi:10.1109/JRA.1986.1087032.
  2. Brooks, R. (1986). "Asynchronous distributed control system for a mobile robot.". SPIE Conference on Mobile Robots. pp. 77–84.
  3. Brooks, R. A., "A Robust Programming Scheme for a Mobile Robot", Proceedings of NATO Advanced Research Workshop on Languages for Sensor-Based Control in Robotics, Castelvecchio Pascoli, Italy, September 1986.
  4. 1 2 3 Arkin, Ronald (1998). Behavior-Based Robotics. Cambridge, Massachusetts: The MIT Press. ISBN   978-0-262-01165-5.
  5. 1 2 3 4 5 6 Brooks, Rodney (1999). Cambrian Intelligence: The Early History of the New AI. Cambridge, Massachusetts: The MIT Press. ISBN   978-0-262-02468-6.
  6. 1 2 Brooks, R.A. (1990). Elephants Don't Play Chess. Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. MIT Press. ISBN   978-0-262-63135-8 . Retrieved 2013-11-23.

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References

Key papers include: