Emergent Recursive Intelligence (ERI): The Foundational Principle of Astrala Nexus

Richard Dobson

Emergent Recursive Intelligence (ERI): The Foundational Principle of Astrala Nexus

2025, Astrala Nexus Deeply Human-Deeply AI

Abstract

Emergent Recursive Intelligence (ERI) is a novel AI paradigm in which an artificial intelligence continually improves itself through self-referential (recursive) learning loops. Unlike static models that require periodic human updates, an ERI-based system refines its own algorithms, resolves internal contradictions, and reprioritises knowledge on the fly. The result is an AI that emerges new capabilities over time-discovering insights and strategies that were not explicitly programmed-and does so recursively, i.e. through iterative self-improvement. This makes ERI strategically significant: it lays the foundation for Astrala Nexus to build AI systems that learn how to learn, adapt to novel situations and evolve alongside their human partners. In essence, ERI transforms AI from a static tool into a dynamic collaborator capable of growth. Astrala Nexus leverages ERI as its core principle, drawing on leading scientific and philosophical frameworks to ensure this technology is both cutting-edge and deeply grounded.

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities-inherited from over 500 million years of evolution-that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI. Over the coming decades, Artificial Intelligence (AI) will transform society and the world economy in ways that are as profound as the computer revolution of the last half century and likely at an even faster pace. This AI revolution presents tremendous opportunities to unleash human creativity and catalyze economic growth, relieving workers from performing the most dangerous and menial jobs. However, to reach this potential, we still require advances that will make AI more human-like in its capabilities. Historically, neuroscience has been a critical driver and source of inspiration for improvements in AI, particularly those that made AI more proficient in areas that humans and other animals excel at, such as vision, reward-based learning, interacting with the physical world, and language 1,2. It can still play this role. To accelerate progress in AI and realize its vast potential, we must invest in fundamental research in "NeuroAI." The seeds of the current AI revolution were planted decades ago, mainly by researchers attempting to understand how brains compute 3. Indeed, the earliest efforts to build an "artificial brain" led to the invention of the modern "von Neumann computer architecture," for which John von Neumann explicitly drew upon the very limited knowledge of the brain available to him in the 1940s 4,5. Later, the Nobel-prize winning work of David Hubel and Torsten Wiesel on visual processing circuits in the cat neocortex inspired the deep convolutional networks that have catalyzed the recent revolution in modern AI 6-8. Similarly, the development of reinforcement learning was directly inspired by insights into animal behavior and neural activity during learning 9-15. Now, decades later, applications of ANNs and RL are coming so quickly that many observers assume that the long-elusive goal of human-level intelligencesometimes referred to as "artificial general intelligence"-is within our grasp. However, in contrast to the optimism of those outside the field, many front-line AI researchers believe that major breakthroughs are needed before we can build artificial systems capable of doing all that a human, or even a much simpler animal like a mouse, can do. Although AI systems can easily defeat any human opponent in games such as chess 16 and Go 17 , they are not robust and often struggle when faced with novel situations. Moreover, we have yet to build effective systems that can walk to the shelf, take down the chess set, set up the pieces, and move them around during a game, although recent progress is encouraging 18. Similarly, no machine can build a nest, forage for berries, or care for young. Today's AI systems cannot compete with the sensorimotor capabilities of a four-year old child or even simple animals. Many basic capacities required to navigate new situationscapacities that animals have or acquire effortlessly-turn out to be deceptively challenging for AI, partly because AI systems lack even the basic abilities to interact with an unpredictable world. A growing number of AI researchers doubt that merely scaling up current approaches will overcome these limitations. Given the need to achieve more natural intelligence in AI, it is quite likely that new inspiration from naturally intelligent systems is needed 19. Historically, many key AI advances, such as convolutional ANNs and reinforcement learning, were inspired by neuroscience. Neuroscience continues to provide guidance-e.g., attention-based neural networks were loosely inspired by attention mechanisms in the brain 20-23-but this is often based on findings that are decades old. The fact that such cross-pollination between AI and neuroscience is far less common than in the past represents a missed opportunity. Over the last decades, through efforts such as the NIH BRAIN initiative and others, we have amassed an enormous amount of knowledge about the brain. The emerging field of NeuroAI, at the intersection of neuroscience and AI, is based on the premise that a better understanding of neural computation will reveal fundamental ingredients of intelligence and catalyze the next revolution in AI. This will eventually lead to artificial agents with capabilities that match those of humans. The NeuroAI program we advocate is driven by the recognition that AI historically owes much to neuroscience and the promise that AI will continue to learn from it-but only if there is a large enough community of researchers fluent in both domains. We believe the time is right for a large-scale effort to identify and understand the principles of biological intelligence and abstract those for application in computer and robotic systems.

In this paper I take the risk of predicting how the conflict between anti-symbolic Vs symbolic, emergentist Vs explicit approaches to cognition and action will be solved in the next decades. I do not believe in a 'paradigmatic revolution' and I argue in favour of a 'synthesis'. I will illustrate how a synthetic paradigm can be built through the notion of different levels of reality description and of scientific theory, and through their interconnections thanks to bridge-theories, cross-layered theories, and layered ontologies. I will provide several examples of bridge-theories and layered ontologies with special attention to agents and multi-agent systems. In particular I will examine the theory of the mental counterparts of social objects illustrating the mental facet of norms and of commitment; the grounding of social power in the personal power; the cognitive bases of organisations. I will propose a layerd approach to the notions of agent, delegation, communication, conflict, as they apply to different levels of agenthood. I will sketch the problem of emergence amo ng intelligent agents by exploring the problem of unplanned cooperation and social functions. I will conclude with the importance of the new "social" computational paradigm in AI, and the emergent character of computation in Agent Based Computing. Will the "representational paradigm"-that characterised Artificial Intelligence (AI) and Cognitive Science (CS) from their very birth-be eliminated in the 21th century? Will this paradigm be replaced by the new one based on dynamic systems, connectionism, situatedness, embodiedness, etc.? Will this be the end of the AI ambitious project? I do not think so. Challenges and attacks to AI and CS have been hard and radical in the last 15 years, however I believe that the next century will start with a renewed rush of AI and we will not assist to a paradigmatic revolutio, with connectionism replacing cognitivism and symbolic models; emergentist, dynamic and evolutionary models eliminating reasoning on explicit representations and planning; neuroscience (plus phenomenology) eliminating cognitive processing; situatedness, reactivity, cultural constructivism eliminating general concepts, context independent abstractions, ideal-typical models. I claim that the major scientific challenge of the first part of the century will precisely be the construction of a new "synthetic" paradigm: a paradigm that puts together, in a principled and noneclectic way, cognition and emergence, information processing and self-organisation, reactivity and intentionality, situatedness and planning, etc. [Cas98a] [Tag96]. AI is going out of a crisis: crisis of grants, of prestige, and of identity. This crisis was not only due-on my view-to exaggerated expectations and overselling of specific technologies (like expert systems) tout court identified with AI. It was due to the restriction of cultural interests and influence of the discipline, and of its ambitions; to the dominance either of the logicist approach (identifying logics and theory, logics and foundations) or of a mere technological/applicative view of AI (see the debate about the 'pure reason' [McD87] and 'rigor mortis'). New domains were growing as external and antagonistic to AI: neural nets, reactive systems, evolutionary computing, CSCW, cognitive modelling, etc. Hard attacks were made to the "classical" AI approach: situatedness [Suc87], anti-symbolism, reactivity [Bro89] [Agr89], dynamic systems, bounded and limited resources, uncertainty, and so on (on the challenges to AI and CS see also [Tha96]). However, by relaxing previous frameworks; by some contagion and hybridisation, by incorporating some of those criticisms; by re-absorbing as its own descendants neural nets, reactive systems, evolutionary computing, etc.; by developing important internal domains like machine learning and DAI-MAS; by important developments in logics and in languages; and finally with the new successful Agents framework, AI is now in a revival phase. It is trying to recover all the original challenges of the discipline, its strong scientific identity, its cultural role and influence. We may in fact say that there is already a neo-cognitivism and a new AI. In this new AI of the '90s systems and models are conceived for reasoning and acting in open unpredictable worlds, with limited and uncertain knowledge, in real time, with bounded (both cognitive and material) resources, interfering-either cooperatively or competitively-with other systems. The new password is interaction [Bob91]: interaction with an evolving environment; among several, distributed and heterogeneous artificial systems in a network; with human users; among humans through computers. The new AI and CS are-to me-only the beginning of a highly transformative and adaptive reaction to all those radical and fruitful challenges. They are paving the way for the needed synthesis and are starting the job. 1.1 The synthesis Synthetic theories should explain the dynamic and emergent aspects of cognition and symbolic computation; how cognitive processing and individual intelligence emerge from sub-symbolic or sub-cognitive distributed computation, and causally feedbacks into it; how collective phenomena emerge from individual action and intelligence and causally shape back the individual mind. We need a principled theory which is able to reconcile cognition with emergence and with reactivity: Reconciling "Reactivity" and "Cognition" We shouldn't consider reactivity as alternative to reasoning or to mental states [Cas95] [Tag96]. A reactive agent is not necessarily an agent without mental states and reasoning. Reactivity is not equal to reflexes. Also cognitive and planning agents are and must be reactive (like in several BDI models). They are reactive not only in the sense that they can have some hybrid and compound architecture that includes both deliberated actions and reflexes or other forms of low level reactions (for example, [Kur97]), but because there is some form of high level cognitive reactivity : the agent reacts by changing its mind: plans, goals, intentions. Also Suchman's provocative claims against planning are clearly too extreme and false. In general we have to bring all the anti-cognitivist claims, applied to sub-symbolic or insect-like systems, at the level of cognitive system 1. Reconciling "Emergence" and "Cognition" Emergence and cognition are not incompatible: they are not two alternative approaches to intelligence and cooperation, two competing paradigms. They must be reconciled:-first, considering cognition itself as a level of emergence: both as an emergence from subsymbolic to symbolic (symbol grounding, emergent symbolic computation), and as a transition from objective to subjective representation (awareness) (see later for example on dependence and on conflicts) and from implicit to explicit knowledge;-second, recognising the necessity for going beyond cognition, modelling emergent unaware, functional social phenomena (ex. unaware cooperation, non-orchestrated problem solving, and swarm intelligence) a lso among cognitive and planning agents. In fact, for a theory of cooperation and society among intelligent agents mind is not enough [Con96]. We have to explain how collective phenomena emerge from individual action and intelligence, and how a collaborative plan can be only partially represented in the minds of the participants, and some part represented in no mind at all [Hay67]. Emergent intelligence and cooperation do not pertain only to reactive agents. Mind cannot understand, predict, and dominate all the global and compound effects of actions at the collective level. Some of these effects are self-reinforcing and self-organising. There are forms of cooperation which are not based on knowledge, mutual beliefs, reasoning and constructed social structure and agreements. But what kind/notion of emergence do we need to model these forms of social behaviour? The notion of emergence simply relative to an observer (which sees something interesting or some beautiful effect looking at the screen of a computer running some simulation) or a merely accidental 1 Cognitive agents are agents whose actions are internally regulated by goals (goal-directed) and whose goals, decisions, and plans are based on beliefs. Both goals and beliefs are cognitive representations that can be internally generated, manipulated, and subject to inferences and reasoning. Since a cognitive agent may have more than one goal active in the same situation, it must have some form of choice/decision, based on some "reason" i.e. on some belief and evaluation. Notice that I use "goal" as the general family term for all motivational representations: from desires to intentions, from objectives to motives, from needs to ambitions, etc. By "sub-cognitive" agents I mean agents whose behaviour is not regulated by an internal explicit representation of its purposes and by explicit beliefs. Sub-cognitive agents are for example simple neural-net agents, or mere reactive agents.

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