January 24, 2007
5.1 | Outline of Topics | ||
Coarse-grain mind model of intelligent systems | |||
Why do we need to study architecture in A.I.? | |||
Architecture and the perception-action loop | |||
Mutliple levels of processing | |||
Realtime | |||
Subsumption architecture: What it is | |||
Subsumption architecture: What it is | |||
Subsumption architecture Basics / Examples | |||
Subsumption architecture Examples II | |||
Subsumption Architecture: Characteristics | |||
Where are we headed? | |||
Large integrated systems | |||
Remote Agent | |||
Brain research architecture: Kosslyn & Koenig | |||
HONDA Asimo | |||
Tools | |||
5.2 |
Coarse-grain mind model of intelligent systems | |
Sensation | What comes in through the senses - "raw information" (processed data) | |
Perception | Includes interpretation and decisions about the sensory data | |
Interpretation | Interpret the perceptual data in context with knowledge and experience | |
Decision | Interpretation, decision making and planning are often intermixed. A simplification includes an initial decision between interpretation and planning, the decision to act. | |
Planning | The (mental and physical) act of deciding future actions | |
Action | The execution of a decision/plan | |
Goals | It is generally considered necesssary to model all cognitive agents as having goals; without goals comparisons between choices cannot be made and decisions become random. | |
Memory | Memory is "everywhere" in a mind. Various memories serve various purposes. Nobody knows how the human memory works as a whole. | |
5.3 |
Why do we need to study architecture in A.I.? | |
(sw)architecture = (hw)architecture | Architecture in A.I. serves the same purpose as architecture when building physical structures Just like in architectures for physical structures, A.I. architectures:
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A.I. systems get exceedingly complicated | A.I. is among the "new" sciences of complexity: Their subject is more complex than any studied by science to date | |
intelligence = organization | Intelligence is in essence an organizational matter. An intelligence is defined by components:
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Science | Complex systems are the next challenge | |
Engineering | We are headed towards larger and larger systems | |
Holistic intelligence | Architecture is now among the main stumbling blocks towards understanding holistic intelligence | |
All naturally intelligent systems perceive, think and act | Perception-Action is about architecture | |
5.4 | Architecture & the Perception-Action Loop | |
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The old pipeline model of
cognitive processing. |
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The need for mental "threads" | To monitor one's own actions one has to sample the world after one has done an Act; sampling of multiple acts becomes a difficult problem in a pipeline model. To cope with this the mind must be doing "load balancing" among "multiple threads". |
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5.5 |
Multiple Levels of Processing |
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The new model of layered continuous processing and execution. The top layer provides a quick response time to time-critical perceptual events (a bus coming at you); the middle layer provides a somewhat more thought-out respone set (1-3 second range) while the bottom layer provides a lot of different processing that takes quite a bit more time (e.g. remembering the name of that singer from the 50s). |
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Coordination hierachies: A functional hierarchy organizes the execution of tasks according to their functions. A product hierarchy organizes production in little units, each focused on a particular product. Several types of markets exist - here two idealized versions are show, without and with brokers. De-centralized markets require more intelligence to be present in the nodes, which can be aleviated by brokers. Brokers, however, present weak points in the system: If you have a system with only 2 brokers mediating between processors and consumers/buyers, failure in these 2 points will render the system useless. Notice that in a basic program written in C++ every single character is such a potential point of failure, which is why bugs are so common in standard software. The human and animal minds are probably ... a mixture of all of these. At the gross anatomical level the brain is a functional hierarchy, with motor control and perceptual inputs in specific places (vision, for example, is always in the back of your head -- no execptions, while language is in the left hemisphere in most people).
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5.6 |
Realtime | |
Perception-Action Loop | A question of realtime | |
Realtime just means "real fast". Right? |
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To achieve realtime |
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5.7 | Subsumption architecture: What it is | |
What it is | Robot control architecture system developed at the MIT AI Lab by Rodney Brooks | |
why it exists | An effort to shift attention from human-level intelligence to simpler organisms, and in the process create the simplest possible architecture that could express intelligent behavior. | |
how we will use it | You will study the subsumption architecture to a sufficient degree that you can implement it in Java or C++ to control the CADIA Hexapod in your final project. | |
5.8 | Subsumption Architecture Basics / Examples | |
Augmented Finite State Machines (AFSMs) | Finite State Machines, augmented with timers | |
Modules (FSMs) have internal state | The internal state includes:
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External environment constists of connections ("wires") |
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Augmented Finite State Machine (AFSM) with connections | ||
Suppressor: Replaces the input to the
module Inhibitor: Stops the output for a given period Reset: Initialization puts the module in its original state |
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Augmentation | The finite state machines are augmented with timers. The time is fixed for each I or R, per module. |
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Timers | Timers enable modules to behave autonomously based on a (relative) time | |
The AFSMs are arranged in "layers" | Layers separate functional parts of the architecture from each other | |
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Level 0 example. |
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Level 0 and 1 combined. |
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Level 0, 1 and 2 combined. |
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5.9 |
Subsumption Architecture Examples II | |
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Example subsumption architecture
with 5 layers. |
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Example subsumption architecture. |
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5.10 |
Subsumption Architecture: Characteristics | |
Architectural characteristics |
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Behavioral characteristics |
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Benefits |
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Drawbacks | Architectures tend to be uniform - it is difficult to extend them beyond controlling simple creatures. Hard to do:
... i.e., it is difficult to construct large architectures that have features of human-level intelligence |
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Drawbacks can be overcome through use of Constructionist AI |
Use well-known mechanisms to solve problems they apply well to
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Constructionist AI results in hybrid architectures | "Hybrid architectures"
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5.11 |
Where are we headed? (next 10-20 years) | |
Large integrated systems |
Need to build integrated simulations of thinking systems to:
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Distributed systems | No single computer is up to the task, at present | |
Manual construction |
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5.12 | Large Integrated Systems | |
Increased autonomy … | …means we are moving towards systems that:
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Industry examples |
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Research examples |
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5.13 | Remote Agent | |
Deep Space One | NASA satellite went into orbit on Oct. 24th
1998 |
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Deep Space One Remote Agent |
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Deep Space 1 Remote Agent
architecture of main software modules. |
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Remote Agent Thought Process Example | Remote Agent knows that:
If a physical thing gets stuck you can try to get it loose by “jiggling” it
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To think, Remote Agent uses: |
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Kosslyn & Koenig architecture. Based on decades of brain research.
5.14 |
Large Complex Systems: HONDA Asimo | |
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Significant progress in last 5-10 years | balance, walking, running | |
Asimo slated to become a household helper by 2012 |
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Asimo is an integrated system | Perception, action, balance, planning, object recognition, etc. | |
Snapshots from HONDA Asimo video showing Asimo walk out of a subway station in New York City.
5.15 |
Tools | |
The future is clear: | We need to build large-scale, distributed architectures to study and develop integrated artificial intelligence | |
Significant lack of good tools to build large-scale, distributed architectures | Most tools in computer science focused on
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Psyclone+OpenAIR |
System explicitly created for large-scale AI development Builds on Constructionist AI |
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