To surmount the notorious difficulties of defining life, we should evaluate theories of life not by whether they provide necessary and sufficient conditions for our current preconceptions about life but by how well they explain living phenomena and how satisfactorily they resolve puzzles about life. On these grounds, the theory of life as supple adaptation (Bedau 1996) gets support from its natural and compelling resolutions of the following four puzzles: (1) How are different forms of life at different levels of (...) the vital hierarchy related? (2) Is there a continuum between life and non-life? (3) Does life essentially concern a living entity’s material composition or its form? (4) Are life and mind intrinsically connected? (shrink)

Researchers in artificial intelligence (AI) Moore’s law states that computational complexity of the models is still far below that and artificial life (Alife) are interested resources for a fixed price roughly double of any living system. New experiments in evo- in understanding the properties of liv- every 18 months. From about 1975 into the lution simulate spatially isolated populations ing organisms so that they can build artificial early 1990s all the gains of Moore’s law went to investigate speciation. Over the (...) past few systems that exhibit these properties for into the changeover from the centralized years, new directions have emerged in AI⁵, in useful purposes. AI researchers are interest- mainframe to the individual computer on attempts to implement artificial creatures in ed mostly in perception, cognition and your desk, accommodating a vastly simulated or physical environments. generation of action (Box 1), whereas Alife increased number of users. The amount of Often called the behaviour-based focuses on evolution, reproduction, computing power available to the individual approach, this new mode of thought involves morphogenesis and metabolism (Box 2). scientist did not change that much, although the connection of perception to action with Neither of these disciplines is a conventional the price came down by a factor of a little in the way of intervening representa- science; rather, they are a mixture of science thousand. But since the early 1990s, all of tional systems. Rather than relying on and engineering. Despite, or perhaps Moore’s law has gone into increasing the per- search, this approach relies on the correct because of, this hybrid structure, both disci- formance of the workstation itself. short, fast connections being present plines have been very successful and our And both AI and Alife have benefited from between sensory and motor modules. world is full of their products. this shift. Behaviour-based approaches began with Every time we use a computer we use Increased computer power has enabled insect models, but more recently they have algorithms and techniques developed by AI search-based AI to push ahead with been extended to humanoid robots⁶ — researchers. (shrink)

Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...) producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. (shrink)

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous (...) one, which control a six legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented. the robot’s performance and behavior repetoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical disabling problems have been ignored. (shrink)