Vision-based Learning for Drones: A Survey

Drones as advanced cyber-physical systems are undergoing a transformative shift with the advent of vision-based learning, a field that is rapidly gaining prominence due to its profound impact on drone autonomy and functionality. Different from existing task-specific surveys, this review offers a comprehensive overview of vision-based learning in drones, emphasizing its pivotal role in enhancing their operational capabilities under various scenarios. We start by elucidating the fundamental principles of vision-based learning, highlighting how it significantly improves drones' visual perception and decision-making processes. We then categorize vision-based control methods into indirect, semi-direct, and end-to-end approaches from the perception-control perspective. We further explore various applications of vision-based drones with learning capabilities, ranging from single-agent systems to more complex multi-agent and heterogeneous system scenarios, and underscore the challenges and innovations characterizing each area. Finally, we explore open questions and potential solutions, paving the way for ongoing research and development in this dynamic and rapidly evolving field. With growing large language models (LLMs) and embodied intelligence, vision-based learning for drones provides a promising but challenging road towards artificial general intelligence (AGI) in 3D physical world

Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures

The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simulating the processes of ontophylogenetic synthesis of multi-agent neurocognitive architectures has been developed and experiments have been carried out to create phenotypes of intelligent agents based on them. A complex genome of an intelligent agent has been developed, the features of a multichromosome genetic algorithm for organizing calculations in the paradigm of multigenerational optimization of multiagent neurocognitive architectures have been established and substantiated. It is shown that multigenerational optimization of the multi-agent neurocognitive architecture of intelligent agents can contribute to the achievement of adaptive resistance to the operating conditions of a general artificial intelligence agent, provide the synthesis of its suboptimal structural and functional scheme, accelerate learning and algorithms for finding solutions to a universal range of problems solved by this agent in its ecological niche

An Ontological-based Knowledge-Representation Formalism for Case-Based Argumentation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-014-9524-3[EN] In open multi-agent systems, agents can enter or leave the system, interact, form societies, and have dependency relations with each other. In these systems, when agents have to collaborate or coordinate their activities to achieve their objectives, their different interests and preferences can come into conflict. Argumentation is a powerful technique to harmonise these conflicts. However, in many situations the social context of agents determines the way in which agents can argue to reach agreements. In this paper, we advance research in the computational representation of argumentation frameworks by proposing a new ontologicalbased, knowledge-representation formalism for the design of open MAS in which the participating software agents are able to manage and exchange arguments with each other taking into account the agents’ social context. This formalism is the core of a case-based argumentation framework for agent societies. In addition, we present an example of the performance of the formalism in a real domain that manages the requests received by the technicians of a call centre.This work is supported by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO II/2013/019].Heras Barberá, SM.; Botti, V.; Julian Inglada, VJ. (2014). An Ontological-based Knowledge-Representation Formalism for Case-Based Argumentation. Information Systems Frontiers. 1-20. https://doi.org/10.1007/s10796-014-9524-3S120Amgoud, L. (2005). An argumentation-based model for reasoning about coalition structures. In 2nd international workshop on argumentation in multi-agent systems, argmas-05(pp. 1–12). Springer.Amgoud, L., Dimopolous, Y., Moraitis, P. (2007). A unified and general framework for argumentation-based negotiation. 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In Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, AAMAS-08 (Vol. 2, pp. 681–688). ACM Press.Chesñevar, C., McGinnis, J., Modgil, S., Rahwan, I., Reed, C., Simari, G., South, M., Vreeswijk, G., Willmott, S. (2006). Towards an argument interchange format. The Knowledge Engineering Review, 21(4), 293–316.Diaz-Agudo, B., & Gonzalez-Calero, P.A. (2007). Ontologies: A handbook of principles, concepts and applications in information systems, integrated series in information systems, chap. an ontological approach to develop knowledge intensive cbr systems (Vol. 14, pp. 173–214). Springer.Dung, P.M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and N -person games. Artificial Intelligence, 77, 321–357.Ferber, J., Gutknecht, O., Michel, F. (2004). From agents to organizations: An organizational view of multi-agent systems. In Agent-oriented software engineering VI, LNCS (Vol. 2935, pp. 214–230.) Springer-Verlag.Hadidi, N., Dimopolous, Y., Moraitis, P. (2010). Argumentative alternating offers. In 9th international conference on autonomous agents and multiagent systems, AAMAS-10 (pp. 441–448). IFAAMAS.Heras, S., Atkinson, K., Botti, V., Grasso, F., Julián, V., McBurney, P. (2010). How argumentation can enhance dialogues in social networks. In Proceedings of the 3rd international conference on computational models of argument, COMMA-10, frontiers in artificial intelligence and applications (Vol. 216, pp. 267–274). IOS Press.Heras, S., Botti, V., Julián, V. (2011). On a computational argumentation framework for agent societies. In Argumentation in multi-agent systems (pp. 123–140). Springer.Heras, S., Botti, V., Julián, V. (2012). Argument-based agreements in agent societies. Neurocomputing, 75(1), 156–162.Heras, S., Jordán, J., Botti, V., Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82–108.Jordán, J., Heras, S., Julián, V. (2011). A customer support application using argumentation in multi-agent systems. In 14th international conference on information fusion (FUSION-11) (pp. 772– 778).Karunatillake, N.C. (2006). Argumentation-based negotiation in a social context. Ph.D. thesis, School of Electronics and Computer Science, University of Southampton, UK.Karunatillake, N.C., Jennings, N.R., Rahwan, I., McBurney, P. (2009). Dialogue games that agents play within a society. Artificial Intelligence, 173(9-10), 935–981.Kraus, S., Sycara, K., Evenchik, A. (1998). Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 104, 1–69.López de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M., Forbus, K., Keane, M., Watson, I. (2006). Retrieval, reuse, revision, and retention in CBR. The Knowledge Engineering Review, 20(3), 215–240.Luck, M., & McBurney, P. (2008). Computing as interaction: Agent and agreement technologies. In IEEE international conference on distributed human-machine systems. IEEE Press.Oliva, E., McBurney, P., Omicini, A. (2008). Co-argumentation artifact for agent societies. In 5th international workshop on argumentation in multi-agent systems, Argmas-08 (pp. 31–46). Springer.Ontañón, S., & Plaza, E. (2007). Learning and joint deliberation through argumentation in multi-agent systems. In 7th international conference on agents and multi-agent systems, AAMAS-07. ACM Press.Ontañón, S., & Plaza, E. (2009). Argumentation-based information exchange in prediction markets. In Argumentation in multi-agent systems, LNAI (vol. 5384, pp. 181–196). Springer.Parsons, S., Sierra, C., Jennings, N.R. (1998). Agents that reason and negotiate by arguing. Journal of Logic and Computation, 8(3), 261–292.Prakken, H. (2010). An abstract framework for argumentation with structured arguments. Argument and Computation, 1, 93–124.Prakken, H., Reed, C., Walton, D. (2005). Dialogues about the burden of proof. In Proceedings of the 10th international conference on artificial intelligence and law, ICAIL-05 (pp. 115–124). ACM Press.Sierra, C., Botti, V., Ossowski, S. (2011). Agreement computing. KI - Künstliche Intelligenz 10.1007/s13218-010-0070-y .Soh, L.K., & Tsatsoulis, C. (2005). A real-time negotiation model and a multi-agent sensor network implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215–271.Walton, D., Reed, C., Macagno, F. (2008). Argumentation schemes. Cambridge University Press.Wardeh, M., Bench-Capon, T., Coenen, F.P. (2008). PISA - pooling information from several agents: Multiplayer argumentation from experience. In Proceedings of the 28th SGAI international conference on artificial intelligence, AI-2008 (pp. 133–146). Springer.Wardeh, M., Bench-Capon, T., Coenen, F.P. 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A Study of AI Population Dynamics with Million-agent Reinforcement Learning

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International Conference on Autonomous Agents and Multiagent Systems

Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty