Papers on Requirements

Papers on Requirements, to Guide Research in
Artificial Intelligence/Robotics/Cognitive Systems
Aaron Sloman

Last updated: 13 Jan 2007
Re-formatted: 25 Jul 2020 -- pdf version added
https://www.cs.bham.ac.uk/research/projects/cogaff/requirements.html
https://www.cs.bham.ac.uk/research/projects/cogaff/requirements.pdf

Introduction

I recently realised that most of what I have written since 1971 can be viewed as contributing to a specification of requirements for human-like, or more generally animal-like, machines, especially robots that can see and interact with the environment around them. This realisation came with a conjecture that one of the main reasons for the many failed predictions since the early days of AI is that most researchers regard the requirements as obvious, thinking that the remaining task is to devise designs and implementations that will meet those requirements.

Thus, many of them attempt to estimate how soon the requirements will be met without attempting to work out in any detail what the requirements actually are. And since the requirements are full of hidden subtlety and complexity that often goes unnoticed, the predictions are wildly over-optimistic. In contrast, what I wrote in a book published in 1978, probably looked wildly pessimistic to some:

"The reasons for saying that existing computer models cannot be accepted as explaining how people do things include:

People perform the tasks in a manner which is far more sensitive to context, including ulterior motives, emotional states, degree of interest, physical exhaustion, and social interactions. Context may affect detailed strategies employed, number of errors made, kinds of errors made, speed of performance, etc.
People are much more flexible and imaginative in coping with difficulties produced by novel combinations, noise, distortions, missing fragments, etc. and at noticing short cuts and unexpected solutions to sub-problems.
People learn much more from their experiences.
People can use each individual ability for a wider variety of purposes: for instance we can use our ability to perceive the structure in a picture like Figure 1 to answer questions about spaces between the letters, to visualise the effects of possible movements, to colour in the letters with different paints, or to make cardboard cut-out copies. We can also interpret the dots in ways which have nothing to do with letters, for instance seeing them as depicting a road map.
More generally, the mental processes in people are put to a very wide range of practical uses, including negotiating the physical world, interacting with other individuals, and fitting into a society. No existing program or robot comes anywhere near matching this.

These discrepancies are not directly attributable to the fact that computers are not made of neurons, or that they function in an essentially serial or digital fashion, or that they do not have biological origins. Rather they arise mainly from huge differences in the amount and organisation of practical and theoretical knowledge, and the presence in people of a whole variety of computational processes to do with motives and emotions which have so far hardly been explored."
(From section 9.13 of Chapter 9 of my 1978 book The Computer Revolution in Philosophy

A similar theme pervades a paper written in 1982 for the Rank Prize Fund conference on image interpretation:

"There is a great discrepancy between the kinds of tasks that can be performed by existing computer models and the experienced richness and multiple uses of human vision. This is not merely a quantitative difference which might easily be overcome by the use of better hardware. There are too many limitations in our theoretical understanding for technological advances to make much immediate difference. Given computers many times faster and bigger than now, and much better TV cameras, we still would not know how to design the visual system for a robot which could bath the baby or clear away the dinner things, let alone enjoy a ballet."
in Image interpretation: The way ahead? in Physical and Biological Processing of Images Editors: O.J.Braddick and A.C. Sleigh. Pages 380--401, Springer-Verlag (1982).

Work on requirements

The work on requirements has had many strands, covering topics such as

Work on human modes of reasoning and the varieties of forms of representation we use -- the topic of my first AI paper in 1971, and several more since then.
Requirements for architectures capable of supporting human-like capabilities, for interacting with a complex and partly unpredictable environment, for performing concurrent tasks, for switching focus of attention, for learning from what happens, etc. These architectural requirements were discussed in Chapter 6 of the 1978 book.
(Based on a paper written in 1973 while visiting Edinburgh University to learn about AI.)
Requirements for human-like vision, involving perception of structure at different levels of abstraction, using different ontologies, with concurrent bottom up and top-down processing, discussed in Chapter 9 of the 1978 book. (At that stage I missed the significance of requirements for concurrent perception of processes at different levels of abstraction.)
Requirements for learning about numbers as humans do in Chapter 8 of the 1978 book.
Requirements for machines to have their own motives, as opposed to having only motives given them by programmers, e.g. in section 10.13 of Chapter 10 of the 1978 book.
Requirements for human-like emotions in Why robots will have emotions in 1981, and Towards a grammar of emotions in 1982.
Requirements for the evolution of human-like freedom of choice, in a Usenet Posting in 1988, How to dispose of the free will issue (elaborated as Chapter 2 of Stan Franklin's book Artificial Minds (MIT Press, 1995).
Why we should not be looking for unique solutions, but trying to understand how different sorts of solutions (e.g. different sorts of architectures) are suited to different sets of requirements, e.g. in The structure of the space of possible minds 1984, and later in Exploring design space and niche space (1995)
Requirements for machines to be able to refer to things and to understand symbols they use, e.g. In IJCAI 1985 with a sequel presented at ECAI 1986.
Requirements for sensors and effectors to be shared between many different functions, sometimes concurrently and sometimes sequentially, in The mind as a control system (1993)
Requirements for representational competences to develop prior to the evolution or development of language, in The primacy of non-communicative language (1979).
Requirements for a mixture of innate (pre-configured) and developed (meta-configured) competences, the latter being a result of interactions between the former and the environment. The Altricial-Precocial Spectrum for Robots and its sequel Natural and artificial meta-configured altricial information-processing systems (both with Jackie Chappell).
Requirements for ability to develop orthogonal, recombinable, competences of many kinds
Requirements for architectures that include fully deliberative competences, and many intermediate cases between purely reactive and fully deliberative.
Requirements for understanding both Humean and Kantian notions of causation
Requirements for going beyond sensorimotor-based (somatic) ontologies to using exosomatic ontologies with some arguments based on a rotating necker cube.
Requirements for architectures that include meta-semantic and meta-management competences.
Requirements for architectures that grow themselves.
... to be extended

Maintained by Aaron Sloman
School of Computer Science
The University of Birmingham