In October 2011, the “2nd European Network for Cognitive Systems, Robotics and Interaction”, EUCogII, held its meeting in Groningen on “Autonomous activity in real-world environments”, organized by Tjeerd Andringa and myself. This is a brief personal report on why we thought autonomy in real-world environments is central for cognitive systems research and what I think I learned about it. --- The theses that crystallized are that a) autonomy is a relative property and a matter of degree, b) increasing autonomy of an artificial system from its makers and users is a necessary feature of increasingly intelligent systems that can deal with the real-world and c) more such autonomy means less control but at the same time improved interaction with the syste

Müller, Vincent C.

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Autonomous Cognitive Systems in Real-World Environments:Less Control, More Flexibility and Better InteractionVincent C. Mu¨llerReceived: 15 November 2011 / Accepted: 16 February 2012! Springer Science+Business Media, LLC 2012Abstract In October 2011, the ‘‘2nd European Networkfor Cognitive Systems, Robotics and Interaction’’, EU-CogII, held its meeting in Groningen on ‘‘Autonomousactivity in real-world environments’’, organized by TjeerdAndringa and myself. This is a brief personal report on whywe thought autonomy in real-world environments is centralfor cognitive systems research and what I think I learnedabout it. The theses that crystallized are that (a) autonomyis a relative property and a matter of degree, (b) increasingautonomy of an artificial system from its makers and usersis a necessary feature of increasingly intelligent systemsthat can deal with the real world and (c) more suchautonomy means less control but at the same timeimproved interaction with the system.Keywords Cognitive system ! Autonomy ! Flexibility !Human–computer interaction ! HCICognitive Systems: Flexibility for IntelligenceAs the conference organizers, we formulated the theme ofthe event as follows: ‘‘We aim to build autonomous arti-ficial cognitive systems that are to pursue their goalssuccessfully in real-world environments that cannot befully anticipated, that are not fully known and that changecontinuously, including other agents …’’ [1]. Thisformulation is based on the view that the distinguishingfeature of ‘cognitive’ artificial systems is not so much theuse of higher level, traditionally called ‘cognitive’ features,but rather flexibility in pursuing the goals of the system—and one way to achieve this flexibility is through higherlevel features, such as planning. (the formulation isinspired by the mission statement of the EU Unit ‘‘Cog-nitive Systems and Robotics’’ on http://cordis.europa.eu/fp7/ict/cognition).Traditional programming, including traditional AI pro-gramming, tries to anticipate the perceptual input the sys-tem might face from environments and to provide aresponse to this input. Since sensors are not perfect, actualenvironments cannot be fully sensed and not be fullydescribed, they are often approximated by probabilisticsystems—but the basic setup is the same: we try to providean algorithmic connection between input and output. ‘‘Andwhat else could we possibly do?’’ you might ask. Well, wecould provide the whole system with features that willmake it achieve its goals, which will not only includebodily features, but also algorithms that make success moreprobable, even in environments that we have notanticipated.The extreme case on the one end of the spectrum wouldbe the lookup-table kind of intelligence, which has a finitelist of possible states of the environment in the one-half ofthe table and a specific action (or set of actions) in the otherhalf. This kind of simple reflex system is ‘‘pre-pro-grammed’’ or ‘‘scripted’’. Note, however, that complexcombinations of factors in such a system can produce fairlycomplex behavior that may be ‘surprising’ to the makers.One extreme case on a different end of the spectrum is asystem that has no information-processing capability, butonly responds to its environment due to its other features,e.g., its morphology. In this case, no prior ‘‘scripting’’ takesV. C. Mu¨ller (&)Anatolia College/ACT, 55510 Pylaia, Greecee-mail: vmueller@act.eduURL: http://www.typos.deV. C. Mu¨llerDepartment of Philosophy, FHI, Programme on the Impacts ofFuture Technology, University of Oxford, Oxford OX1 1PT, UK123Cogn ComputDOI 10.1007/s12559-012-9129-4Müller, Vincent C. (2012), ‘Autonomous cognitive systems in real-world environments: Less control, more flexibility and better interaction’, Cognitive Computation, 4 (3), 212-15. http://www.sophia.de - http://orcid.org/0000-0002-4144-4957place, but the system is equally inflexible. (I assume thatinformation-processing, digital or analog, is non-triviallydistinguishable from other causal processes by the presenceof components that play the functional role of representa-tion—a direction that is explored, for example, in [2] or [3,ch. 2].)I think it is important not to characterize the feature offlexibility in terms of ‘action selection’, since this is often amisleading term for the behavior of intelligent systems:The problem ‘‘Which action to select next?’’ stems fromthe ‘‘Model-Plan-Act’’ view of action (and the ‘‘Intention-Belief-Desire’’ psychology)—the standard in classicalrepresentational AI. This view focuses on ‘action’ that isunder voluntary control, which leaves out a large part ofintelligent behavior. Many intelligent agents do not seem to‘select actions’, be they natural (a slug or even a cock-roach) or artificial (non-classical designs, coupled embod-ied systems; e.g., a passive dynamic walker does not selectthe action ‘‘make the next step’’).AutonomyThe demand for flexibility thus results in a demand forautonomy. In a first approximation, autonomy is the abilityof the system to respond to the environment ‘‘by itself’’,without a prior ‘‘script’’. The Greek word ‘‘autonomy’’means the ability to give oneself (e)avto ones own lawnomos, thus becoming avtonomos.However, notions like generating behavior ‘‘by itself’’ orgiving laws ‘‘to oneself’’ are of limited use since the systemis never alone, but is always subject to various causalinfluences from the ‘‘outside’’ (if boundaries can even bespecified). In control engineering, autonomy is defined interms of interference by the user of the system, wheredegrees go from a fully user controlled system to a systemnot controlled at all. (Some systems are adaptive in thisrespect, i.e., change their level of autonomy depending onneeds [cf. 4].) The notion we appear to need is a relationalone:X is autonomous from Y if and only if X pursues itsgoals without input from YThis initial definition can be clarified in at least twoways: The input from Y can be a matter of degree, and thusX can be more or less autonomous from Y. Also, in manycases it will be useful to differentiate that X is autonomousfrom Y with respect to some type of input or some type ofability (e.g., spatial orientation).Note that the Y can be a human being, like the maker oruser of an artificial system, but we can naturally use thesame notion for other agents also: One can say that ananimal is more or less autonomous (e.g., the very young areoften less autonomous) and that autonomy is reduced withrespect to other members of the species, or of other spe-cies—e.g., in a plant that lives in symbiosis with another,or in a microorganism that lives inside a larger organism.On the other hand, if a living being is dependent on aspecific environment, like a particular cave, for example, Iwould not call that a reduction of its autonomy. It followsthat X can be autonomous from Y properly only if both Xand Y are agents.One view that is often heard is that a system is auton-omous to the degree that it can set its own goals. However,a system that ‘‘sets its own goals’’ can do so only withinlimits provided by the unalterable features of its design, or,to put it differently: there must be causes for any givensetting of goals. We say that humans have free will, eventhough we assume that there are causes for our setting ofgoals. If humans consider their goals, decide that they wantto have different goals and adopt them, then this re-settingis dependent on evaluating the present goals on the basis ofhigher-order goals. Indeed, this ability to rationally setgoals and follow them has been considered the core of onecommon explanation for what is meant by the idea ofhuman ‘‘free will’’. This approach typically allows for anotion of responsibility in a world where all events arecaused [5]. It seems, therefore, that the ability to set goalsis just a further degree of autonomy with respect to anothersystem; it is just a form of pursuing higher order goals. Ofcourse, there is the Maturana-Varela tradition, according towhich only living autopoetic systems can truly have goals,but is this relevant for our purposes, the understanding ofautonomy? Perhaps, it marks an outer bound that certaindegrees of autonomy can only be reached by living sys-tems—but I would advise against including this ideology inan initial understanding of the notion (keeping in mind thatany system will have several goals, often on several levelsof description or granularity).Given this discussion, we might accept a formulationlike this:Agent X is autonomous from agent Y to the degreethat X pursues its goals without input from YFlexibility Through Autonomy in an EnvironmentIf we assume that intelligence is the ability to pursue one’sgoals successfully, then it becomes clear that more auton-omy does not necessarily imply more intelligence. In par-ticular, autonomy will only be a useful feature ofintelligence in environments that are not fully anticipated.It is for this reason that in real-world environments, whichcan only be anticipated to a very small degree, autonomy isa crucial feature of intelligence—in fact the crucial feature,Cogn Comput123together with successful use of the environment, includingother agents (and even these features of the environmentare not specifiable ahead of time, so they require autonomy,too). In other words, in the real world, an artificial systemmust be strongly autonomous of its maker and user to besuccessful or intelligent. This stands in stark contrast to thetotally controlled environment of simulation and the highlycontrolled environments of industrial robotics today. (Onemajor reason why the Fukushima disaster happened andwas then dealt with so badly is the absence of autonomousand thus flexible systems in both phases—no current robotcan deal with the inside of the reactor if we cannot specifyin advance what the inside is like [6].)One way to explain this point is to look at the agent ascarrying out a task of some complexity in an environmentof some complexity (Fig. 1).In this space of possibilities, classical robotics providessolutions in the area that is low in y values, but increasinglymore successful in x values—it achieves complex tasks innon-complex environments. Natural systems, on the otherhand, are able to deal with high y values, but achieve rel-atively low scores in the x range—they achieve less com-plex tasks but in complex environments. Humans areprobably an extreme example in terms of their adaptabilityto different environments while also achieving fairly highscores in certain x sectors—but humans remain the ‘‘faultybeings’’, as Hans Gehlen called them, that are surpassed innearly any ability by some animal (except higher cogni-tion). Artificial cognitive systems will enable artificialsystems to ‘detach’ themselves from the y baseline andmove into the higher spaces opened up by y. This moveinto more complex unspecified environments requires moreautonomy on the part of the systems. (This notion owesmuch to the discussions in the 2011 EUCogII workshop‘‘Challenges for Cognitive Systems’’ at Rapperswil.)Autonomy as Loss of Control Versus Autonomyas Gain of InteractionGiven that we have defined autonomy in terms of inde-pendence from other agents, in particular the maker anduser of a system, it follows that more autonomy impliesless control. This loss of control would seem undesirableboth in terms of performance of the system and in terms ofavoiding undesired behavior of the system, which wouldalso be less predictable.I would suggest, however, that these negative effects areoutweighed by positive ones, namely the improved abilityof humans (and other agents) to interact with such systems.In a different context, I have recently made the obviouspoint that human interaction with other agents relies cru-cially on the attribution of intentions or goals to theseagents [7]. We humans can interact successfully with otheragents just when we can interpret their behavior as drivenby their goals, shown by behavior that provides a resistanceto our own. It is impossible to cooperate with another agentthat has no goals, thus offering no such resistance. Suchinteraction will also be a lot easier than technical control—since we humans are already experts at it.I conclude that new human–computer interaction (NewHCI) will be properly a form or interaction, less of control.We must overcome the wish to control the autonomouscognitive system and begin to interact with it.Fig. 1 The complexity spacefor intelligent agentsCogn Comput123References1. Andringa T, Mu¨ller VC. 10–11 October 2011—Fifth EUCogIIMembers Conference, Groningen, ‘‘Autonomous activity in real-world environments’’. Available from: http://www.eucognition.org/index.php?page=fifth-conference-general-info.2. Mu¨ller VC. Is there a future for AI without representation? MindMach. 2007;17(1):101–15.3. Miłkowski M. Explaining the computational mind. Cambridge:MIT Press (forthcoming).4. Sheridan TB. Humans and automation: system design and researchissues. New York: Wiley; 2002.5. Frankfurt H. Freedom of the will and the concept of a person.J Philos 1971;LXVIII(1):5–20.6. Cockburn-Price S, Mu¨ller VC. Robots seek role in reactors:artificial cognitive systems will soon be able to play a greater rolein dealing with problems such as Fukushima. ProfessionalEngineering 24/5, 04.05.2011. Available from: http://profeng.com/news/robots-seek-role-in-reactors_1083.7. Mu¨ller VC. Interaction and resistance: the recognition of intentionsin new human–computer interaction. In: Esposito A, Esposito AM,Martone R, Mu¨ller VC, Scarpetta G, editors. Towards autonomous,adaptive, and context-aware multimodal interfaces: theoretical andpractical issues. Berlin: Springer; 2011. p. 1–7.Cogn Comput123

Autonomous cognitive systems in real-world environments: Less control, more flexibility and better interaction

\u3cp\u3eIn October 2011, the  2nd European Network for Cognitive Systems, Robotics and Interaction , EUCogII, held its meeting in Groningen on  Autonomous activity in real-world environments , organized by Tjeerd Andringa and myself. This is a brief personal report on why we thought autonomy in real-world environments is central for cognitive systems research and what I think I learned about it. The theses that crystallized are that (a) autonomy is a relative property and a matter of degree, (b) increasing autonomy of an artificial system from its makers and users is a necessary feature of increasingly intelligent systems that can deal with the real world and (c) more such autonomy means less control but at the same time improved interaction with the system.\u3c/p\u3

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Autonomous cognitive systems in real-world environments:less control, more flexibility and better interaction

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Autonomous cognitive systems in real-world environments: Less control, more flexibility and better interaction

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