[These are some still-disorganised notes on Robotics, Artificial Intelligence, and Knowledge Representation that will likely be moved over to the wiki once it’s up and running. Likewise, at the bottom are some references, which will also end up on the wiki…]
“SMPA: the sense-model-plan-act framework. See section 3.6 for more details of how the SMPA framework inuenced the manner in which robots were built over the following years, and how those robots in turn imposed restrictions on the ways in which intelligent control programs could be built for them.”— Brooks 1985, p.2
From Brooks “Intelligence Without Reason” [Brooks 1991]:
“There are a number of key aspects characterizing this style of work.
- Situatedness: The robots are situated in the world — they do not deal with abstract descriptions, but with the here and now of the world directly influencing the behavior of the system.
- Embodiment: The robots have bodies and experience the world directly — their actions are part of a dynamic with the world and have immediate feedback on their own sensations.
- Intelligence: They are observed to be intelligent — but the source of intelligence is not limited to just the computational engine. It also comes from the situation in the world, the signal transformations within the sensors, and the physical coupling of the robot with the world.
- Emergence: The intelligence of the system emerges from the system’s interactions with the world and from sometimes indirect interactions between its components — it is sometimes hard to point to one event or place within the system and say that is why some external action was manifested.”
Brooks notes that the evolution of machine intelligence is somewhat similar to biological evolution, with “punctuated equilibria” as a norm, where “there have been long periods of incremental work within established guidelines, and occasionally a shift in orientation and assumptions causing a new subfield to branch off. The older work usually continues, sometimes remaining strong, and sometimes dying off gradually.”
He expands upon these four concepts starting on page 14:
- The key idea from situatedness is: The world is its own best model.
- The key idea from embodiment is: The world grounds regress.
- The key idea from intelligence is: Intelligence is determined by the dynamics of interaction with the world.
- The key idea from emergence is: Intelligence is in the eye of the observer.
I might note that Brooks’ criticisms of the field of Knowledge Representation reflect my own findings, observed during the four years of my doctoral research on KR at the Knowledge Media Institute.
It is my opinion, and also Smith’s, that there is a fundamental problem still and one can expect continued regress until the system has some form of embodiment.— Brooks 1991
The lack of grounding of abstract representation is evident from the almost complete
lack of the KR researchers to even bother to definitively explicate the two terms in the field’s title: “Knowledge” and “Representation”. How can one rationally explore a field when one doesn’t yet know what knowledge is, or where there is no epistemologically-sound definition of the word representation? The greatest related advances in that field belong to the likes of C.S. Peirce, John Dewey, Wilfred Sellars, Richard Rorty and Robert Brandom, but this seems (at this point in time) to be still disconnected to the concept of “embodiment” as explored in robotics (but I’m hardly the person to judge that issue). So it’s grounded neither in mathematics 1 nor in the real world.
I must agree with Brooks, that embodiment is a necessary precondition for research into intelligence. Brooks’ paper was from 1991, my doctoral programme began in 2002. I wish I’d read his paper prior to 2002. I met Doug Lenat in 2000 and over dinner in Austin we discussed the idea of working for his company, Cycorp (the corporate home of the Cyc Ontology). The whole thing is a giant chess set, a massive undertaking that as of 2020 is still essentially doing what it did when I saw it for the first time at SRI in 1979; it’s as Brooks says, it’s just followed the advances in computing technology but not really provided any real breakthroughs.
Regarding scale or size:
“The limiting factor on the amount of portable computation is not weight of the computers directly, but the electrical power that is available to run them. Empirically we have observed that the amount of electrical power available is proportional to the weight of the robot.”— Brooks 1991, p. 18
- [Brooks 1985] A Robust Layered Control System for a Mobile Robot
Rodney A. Brooks, A.I. Memo 864,
MIT Artificial Intelligence Laboratory, September 1985
- [PID] Proportional–Integral–Derivative Controller (PID controller)
- [SR04] SR04 Robot (PDF)
David P. Anderson
-  The Hall effect (named after Edwin Hall’s experiments in the 1870s)
- [Brooks 1991] Intelligence Without Reason,
Rodney Brooks, A.I. Memo No. 1293,
MIT Artificial Intelligence Laboratory, April 1991
- Integrated Systems Based on Behaviors
Rodney A. Brooks, MIT Artificial Intelligence Laboratory
- The Behavior Language; User’s Guide
Rodney A. Brooks, A. I. Memo 1227,
MIT Artificial Intelligence Laboratory, April 1990
- Hierarchical and State-based Architectures for Robot Behavior Planning and Control
Philipp Allgeuer and Sven Behnke, Autonomous Intelligent Systems,
Computer Science Institute VI, University of Bonn, Germany
- A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network
Rodney A. Brooks, A.I. Memo 1091,
MIT Artificial Intelligence Laboratory, February 1989
- Intelligence without Robots: A Reply to Brooks
Oren Etzioni, AI Magazine Volume 14 Number 4 (1993) (© AAAI)
- Is there a Future for AI without Representation?
Vincent C. Müller, Anatolia College/ACT,
Minds and Machines, March 2007, Volume 17, Issue 1, pp 101–115
- Space Mapping and Navigation for a Behaviour-Based Robot
Yoel Gat, Université de Neuchatel doctoral thesis
- Learning in behavior-based multi-robot systems: policies, models, and other agents
Maja J. Mataric, Journal of Cognitive Systems Research 2 (2001) 81–93
- Navigating With a Rat Brain: A Neurobiologically-Inspired Model for Robot Spatial Representation
Maja J. Mataric, MIT Artificial Intelligence Laboratory