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window.loswp.previewableAttachments = [{"id":53834444,"identifier":"Attachment_53834444","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":33854441,"created_at":"2017-07-11T23:50:48.921-07:00","from_world_paper_id":163101577,"updated_at":"2025-02-01T06:53:08.358-08:00","_data":{"ai_abstract":"Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots explores the development of adaptive behaviors in robots through sensory information processing. By leveraging the homeokinetic principle, robots can self-organize their motor controls, allowing for the generation of coherent and effective movements without direct programming. The research highlights an agent-based approach where internal expert networks are utilized to predict control actions, leading to the composition of reusable behavioral primitives that enhance the robot's adaptability in various environments.","ai_title_tag":"Self-Organizing Behavioral Primitives in Robots","publication_date":"2009,,","publication_name":"BMC Neuroscience"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"low","language":"en","title":"Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [41514875]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":53834444,"attachmentType":"pdf"}"><img alt="First page of “Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/53834444/mini_magick20220706-11443-swlf85.png?1657098515" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="41514875" href="https://independent.academia.edu/GeorgMartius"><img alt="Profile image of Georg Martius" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Georg Martius</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2009, BMC Neuroscience</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">2 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 33854441; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=33854441"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--detail ds2-5-body-md">AI-generated Abstract</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots explores the development of adaptive behaviors in robots through sensory information processing. By leveraging the homeokinetic principle, robots can self-organize their motor controls, allowing for the generation of coherent and effective movements without direct programming. The research highlights an agent-based approach where internal expert networks are utilized to predict control actions, leading to the composition of reusable behavioral primitives that enhance the robot's adaptability in various environments.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":53834444,"attachmentType":"pdf","workUrl":"https://www.academia.edu/33854441/Emergence_of_behavioral_primitives_in_self_organizing_control_and_composition_of_behavior_for_autonomous_robots"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":53834444,"attachmentType":"pdf","workUrl":"https://www.academia.edu/33854441/Emergence_of_behavioral_primitives_in_self_organizing_control_and_composition_of_behavior_for_autonomous_robots"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas" data-impression-entity-id="33854441" data-impression-entity-type="2" data-impression-source="signup-banner"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Michael Herrmann</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2008</p><p class="ds-related-work--abstract ds2-5-body-sm">Abstract We describe a learning algorithm that generates behaviors by self-organization of sensorimotor loops in an autonomous robot. The behavior of the robot is analyzed by a multi-expert architecture, where a number of controllers compete for the data from the physical robot. Each expert stabilizes the representation of the acquired sensorimotor mapping in dependence of the achieved prediction error and forms eventually a behavioral primitive.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Structure from behavior in autonomous agents","attachmentId":30737141,"attachmentType":"pdf","work_url":"https://www.academia.edu/2783205/Structure_from_behavior_in_autonomous_agents","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2783205/Structure_from_behavior_in_autonomous_agents"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="114061125" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/114061125/Self_organization_at_the_lowest_level_Proactively_learning_skills_in_autonomous_systems">Self-organization at the lowest level: Proactively learning skills in autonomous systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="288253896" href="https://independent.academia.edu/RichertWilli">Willi Richert</a></div><p class="ds-related-work--abstract ds2-5-body-sm">To enable autonomous systems to learn basic skills for unknown and changing environments and stay robust in case of change, Organic Computing principles have to be applied at all layers. In this work an architecture is presented that can be used at the lowest layer providing robust skills to higher-levelstrategy layers, that depend on encapsulated actions. With emphasis on robustness it is able to learn to control its actors without apriori informationabout their meaning. This is made possible by skill modules that are learned together with their action-effect dependencies and their enabling preconditions by proactively carrying out experiments within their environment. The architecture is evaluated by simulating adifferentially drivenrobot. 1I ntroduction When some form of adaptation is needed typically atinypartinthe overall control architecture is identified and substituted by e. g. neural nets or solved by other statistical learning methods. However, theyall assume astatic training set, which hampers the ability to adapt appropriately to suddenly changing environments. Furthermore, theyexpect the designer to foresee all possible changes the system might undergo. In this work wepresent an architecture that is able to detect changes in the environment that render previously learned skills useless, and react in aw ay that relearns the obsolete parts by proactively carrying out experiments. Thereby,the designer does not have to foresee every possible change the system might undergo. With skills we understand low-levelblocks of behavior that can be triggered by some higher-levelstrategy process. We do this by coupling learned skills with their enabling conditions that have been observed while experimenting and the effects of the action. Thereby the system can monitor progress via manyfi ne-grained cause-effect schemata it has learned, and trigger relearning of the previously learned skill. By developing and finding basic skills the robot drastically reduces the exploration space the higher levels otherwise had to consider. Let us assume an upper strategy layer requesting some behavior that has ac ertain effect on its environment. The skill learning layer then consults its skill database (Skill DB) for appropriate skill modules consisting of aset of preconditions, the action and the predicted effect (similar to start condition, action type, end condition in [Bis05]). If an adequate skill is found, meaning that there is some behavior that resulted in the desired effects</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Self-organization at the lowest level: Proactively learning skills in autonomous systems","attachmentId":110858356,"attachmentType":"pdf","work_url":"https://www.academia.edu/114061125/Self_organization_at_the_lowest_level_Proactively_learning_skills_in_autonomous_systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/114061125/Self_organization_at_the_lowest_level_Proactively_learning_skills_in_autonomous_systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="114997094" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/114997094/On_the_Emergence_of_Intelligent_Global_Behaviors_from_Simple_Local_Actions">On the Emergence of Intelligent Global Behaviors from Simple Local Actions</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37738082" href="https://independent.academia.edu/PaulBenjamin8">Paul Benjamin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal on Artificial Intelligence Tools, 1999</p><p class="ds-related-work--abstract ds2-5-body-sm">Artificial Intelligence focuses on the question of how to design system to exhibit intelligent behaviour in complex environments. Complex global behaviours can emerge from simple systems acting in a complex environment; however, this emergence requires that the systems&#39; internal structure reflect essential structures in the environment. This paper examines the algebraic structure of a system&#39;s actions. We find that these actions often possess a self-similar local neighborhood structure that permits analysis and synthesis to be performed locally yet produce global, intelligent behaviours. A procedure for finding this local structure is presented, and illustrated with examples.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"On the Emergence of Intelligent Global Behaviors from Simple Local Actions","attachmentId":111535635,"attachmentType":"pdf","work_url":"https://www.academia.edu/114997094/On_the_Emergence_of_Intelligent_Global_Behaviors_from_Simple_Local_Actions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/114997094/On_the_Emergence_of_Intelligent_Global_Behaviors_from_Simple_Local_Actions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="40861390" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/40861390/Natural_Intelligence_for_Autonomous_Agents">Natural Intelligence for Autonomous Agents</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="36143786" href="https://independent.academia.edu/ChristianBalkenius">Christian Balkenius</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2000</p><p class="ds-related-work--abstract ds2-5-body-sm">The paper presents a general architecture for behaviour based control systems for autonomous agents. A number of archi tectural principles are proposed which make it possible to combine reactive control with learning and problem solving in a coherent way. In particular, I investigate the interaction between reinforcement learning, internal world models and dynamic action selection as well as a number</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Natural Intelligence for Autonomous Agents","attachmentId":61146960,"attachmentType":"pdf","work_url":"https://www.academia.edu/40861390/Natural_Intelligence_for_Autonomous_Agents","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/40861390/Natural_Intelligence_for_Autonomous_Agents"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="125899669" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/125899669/Contingent_robot_behavior_from_self_referential_dynamical_systems">Contingent robot behavior from self-referential dynamical systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="41268300" href="https://uni-leipzig.academia.edu/RalfDer">Ralf Der</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2005</p><p class="ds-related-work--abstract ds2-5-body-sm">The self-organization of behavior is both a striking phenomenon in living beings and a challenging objective for autonomous robots. In our earlier work we introduced homeokinesis-the dynamical pendant of homeostasis-as a general domain invariant principle for behavioral self-creation. The present paper continues these investigations under a more pragmatic aspect. We start from the formulation of two requirements to the behavior namely that actions are such that (i) they are maximally sensitive reactions to the sensor values and that (ii) the consequences of the actions taken are still predictable. We show how this general statement can be formulated into a concrete error function E measuring the distance between the current and the ideal behavior formulated by the requirements. Gradient descending E produces a self-regulating dynamical system. Mathematical arguments show that the robot behaviors emerging from this are both explorative and sensitive to the environment. From the general principle simple learning rules are derived for the neurons of a closed loop robot controller. These learning rules are shown in a simple application with a physical robot to realize a self-learning autonomous robot which can survive in a sufficiently simple world without any further external help. In particular we demonstrate that sensors are automatically integrated according to their response strength as soon as they deliver a signal to the controller. Moreover the system also can deal with the problem of a rapid change in the properties of the sensors.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Contingent robot behavior from self-referential dynamical systems","attachmentId":119861781,"attachmentType":"pdf","work_url":"https://www.academia.edu/125899669/Contingent_robot_behavior_from_self_referential_dynamical_systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/125899669/Contingent_robot_behavior_from_self_referential_dynamical_systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="47148069" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/47148069/Information_driven_self_organization_the_dynamical_system_approach_to_autonomous_robot_behavior">Information-driven self-organization: the dynamical system approach to autonomous robot behavior</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="65576426" href="https://sydney.academia.edu/MikhailProkopenko">Mikhail Prokopenko</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Theory in Biosciences, 2012</p><p class="ds-related-work--abstract ds2-5-body-sm">In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more fundamental questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open-ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This article studies in some detail the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process. The PI of a process quantifies the total information of past experience that can be used for predicting future events. However, the application of information theoretic measures in robotics mostly is restricted to the case of a finite, discrete state-action space. This article aims at applying the PI in the dynamical systems approach to robot control. We study linear systems as a first step and derive exact results for the PI together with explicit learning rules for the parameters of the controller. Interestingly, these learning rules are of Hebbian nature and local in the sense that the synaptic update is given by the product of activities available directly at the pertinent synaptic ports. The general findings are exemplified by a number of case studies. In particular, in a two-dimensional system, designed at mimicking embodied systems with latent oscillatory locomotion patterns, it is shown that maximizing the PI means to recognize and amplify the latent modes of the robotic system. This and many other examples show that the learning rules derived from the maximum PI principle are a versatile tool for the self-organization of behavior in complex robotic systems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Information-driven self-organization: the dynamical system approach to autonomous robot behavior","attachmentId":66394675,"attachmentType":"pdf","work_url":"https://www.academia.edu/47148069/Information_driven_self_organization_the_dynamical_system_approach_to_autonomous_robot_behavior","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/47148069/Information_driven_self_organization_the_dynamical_system_approach_to_autonomous_robot_behavior"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="126558298" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/126558298/A_Formal_Account_of_Structuring_Motor_Actions_With_Sensory_Prediction_for_a_Naive_Agent">A Formal Account of Structuring Motor Actions With Sensory Prediction for a Naive Agent</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="182295223" href="https://independent.academia.edu/BrunoGas">Bruno Gas</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Frontiers in Robotics and AI, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">For naive robots to become truly autonomous, they need a means of developing their perceptive capabilities instead of relying on hand crafted models. The sensorimotor contingency theory asserts that such a way resides in learning invariants of the sensorimotor flow. We propose a formal framework inspired by this theory for the description of sensorimotor experiences of a naive agent, extending previous related works. We then use said formalism to conduct a theoretical study where we isolate sufficient conditions for the determination of a sensory prediction function. Furthermore, we also show that algebraic structure found in this prediction can be taken as a proxy for structure on the motor displacements, allowing for the discovery of the combinatorial structure of said displacements. Both these claims are further illustrated in simulations where a toy naive agent determines the sensory predictions of its spatial displacements from its uninterpreted sensory flow, which it then uses...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Formal Account of Structuring Motor Actions With Sensory Prediction for a Naive Agent","attachmentId":120418273,"attachmentType":"pdf","work_url":"https://www.academia.edu/126558298/A_Formal_Account_of_Structuring_Motor_Actions_With_Sensory_Prediction_for_a_Naive_Agent","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/126558298/A_Formal_Account_of_Structuring_Motor_Actions_With_Sensory_Prediction_for_a_Naive_Agent"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="91080323" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/91080323/Global_behavior_via_cooperative_local_control">Global behavior via cooperative local control</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="145280162" href="https://independent.academia.edu/CynthiaFerrell">Cynthia Ferrell</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Autonomous Robots, 1995</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Global behavior via cooperative local control","attachmentId":94468260,"attachmentType":"pdf","work_url":"https://www.academia.edu/91080323/Global_behavior_via_cooperative_local_control","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/91080323/Global_behavior_via_cooperative_local_control"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="2783269" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2783269/Reflexes_from_self_organizing_control_in_autonomous_robots">Reflexes from self-organizing control in autonomous robots</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="253979" href="https://edinburgh.academia.edu/DrMichaelHerrmann">J. Michael Herrmann</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2007</p><p class="ds-related-work--abstract ds2-5-body-sm">Abstract Homeokinetic learning provides a route to the self-organization of elementary behaviors in autonomous robots by establishing low-level sensomotoric loops. Strength and duration of the internal parameter changes which are caused by the homeokinetic adaptation provide a natural evaluation of external states, which can be used to incorporate information from additional sensory inputs and to extend the function of the low-level behavior to more general situations.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Reflexes from self-organizing control in autonomous robots","attachmentId":30737120,"attachmentType":"pdf","work_url":"https://www.academia.edu/2783269/Reflexes_from_self_organizing_control_in_autonomous_robots","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2783269/Reflexes_from_self_organizing_control_in_autonomous_robots"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="103493138" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/103493138/Exceeding_Computational_Complexity_Trial_and_Error_Dynamic_Action_and_Intelligence">Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="61050753" href="https://independent.academia.edu/XiongChuyu">Chuyu Xiong</a></div><p class="ds-related-work--metadata ds2-5-body-xs">arXiv (Cornell University), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Computational complexity is a core theory of computer science, which dictates the degree of difficulty of computation. There are many problems with high complexity that we have to deal, which is especially true for AI. This raises a big question: Is there a better way to deal with these highly complex problems other than bounded by computational complexity? We believe that ideas and methods from intelligence science can be applied to these problems and help us to exceed computational complexity. In this paper, we try to clarify concepts, and we propose definitions such as unparticularized computing, particularized computing, computing agents, and dynamic search. We also propose and discuss a framework, i.e., trial-and-error + dynamic search. Number Partition Problem is a well-known NP-complete problem, and we use this problem as an example to illustrate the ideas discussed.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence","attachmentId":103485864,"attachmentType":"pdf","work_url":"https://www.academia.edu/103493138/Exceeding_Computational_Complexity_Trial_and_Error_Dynamic_Action_and_Intelligence","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/103493138/Exceeding_Computational_Complexity_Trial_and_Error_Dynamic_Action_and_Intelligence"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":53834444,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":53834444,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_53834444" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="90692162" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/90692162/Autonomous_Programming_for_General_Purposes_Theory">Autonomous Programming for General Purposes: Theory</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3339155" href="https://michiganstate.academia.edu/JuyangWeng">Juyang Weng</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Humanoid Robotics, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Autonomous Programming for General Purposes: Theory","attachmentId":94184302,"attachmentType":"pdf","work_url":"https://www.academia.edu/90692162/Autonomous_Programming_for_General_Purposes_Theory","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/90692162/Autonomous_Programming_for_General_Purposes_Theory"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="1" data-entity-id="4681088" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4681088/Eclectic_Theory_of_Intelligent_Robots_315_X_Eclectic_Theory_of_Intelligent_Robots">Eclectic Theory of Intelligent Robots 315 X Eclectic Theory of Intelligent Robots</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3770652" href="https://uc.academia.edu/ErnestHall">Ernest L Hall</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Eclectic Theory of Intelligent Robots 315 X Eclectic Theory of Intelligent Robots","attachmentId":32016774,"attachmentType":"pdf","work_url":"https://www.academia.edu/4681088/Eclectic_Theory_of_Intelligent_Robots_315_X_Eclectic_Theory_of_Intelligent_Robots","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/4681088/Eclectic_Theory_of_Intelligent_Robots_315_X_Eclectic_Theory_of_Intelligent_Robots"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="2" data-entity-id="16312510" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/16312510/Automatic_generation_of_an_agents_basic_behaviors">Automatic generation of an agent's basic behaviors</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="35421099" href="https://independent.academia.edu/OBuffet">O. 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Goncalves</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Frontiers in Evolutionary Robotics, 2008</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Learning by Experience and by Imitation in Multi-Robot Systems","attachmentId":61626958,"attachmentType":"pdf","work_url":"https://www.academia.edu/41452832/Learning_by_Experience_and_by_Imitation_in_Multi_Robot_Systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/41452832/Learning_by_Experience_and_by_Imitation_in_Multi_Robot_Systems"><span class="ds2-5-text-link__content">View PDF</span><span 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Maeda</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Frontiers in Computational Neuroscience, 2014</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Learning modular policies for robotics","attachmentId":92934565,"attachmentType":"pdf","work_url":"https://www.academia.edu/89066197/Learning_modular_policies_for_robotics","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/89066197/Learning_modular_policies_for_robotics"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" 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class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="70852170" href="https://independent.academia.edu/MeyerJeanArcady">Jean-Arcady Meyer</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1996</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Learning reactive and planning rules in a motivationally autonomous animat","attachmentId":66655000,"attachmentType":"pdf","work_url":"https://www.academia.edu/47694578/Learning_reactive_and_planning_rules_in_a_motivationally_autonomous_animat","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline 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