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. (2001). Machine learning and inductive logic programming for multi-agent systems, in M. Luck et al. (Eds.), Multi-Agent Systems and Applications, Springer, Berlin/Heidelberg, pp. 246-270. Lin, L.-J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching, Machine Learning 8(3-4): 293-321. Panait, L. and Luke, S. (2005). Cooperative multi-agent learning: The state of the art, Autonomous Agents and Multi-Agent Systems 11(3): 387-434. Quinlan, J. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, CA. Rao, A


In the story Alice in Wonderland, Alice fell down a rabbit hole and suddenly found herself in a strange world called Wonderland. Alice gradually developed knowledge about Wonderland by observing, learning, and reasoning. In this paper we present the system Alice In Wonderland that operates analogously. As a theoretical basis of the system, we define several basic concepts of logic in a generalized setting, including the notions of domain, proof, consistency, soundness, completeness, decidability, and compositionality. We also prove some basic theorems about those generalized notions. Then we model Wonderland as an arbitrary symbolic domain and Alice as a cognitive architecture that learns autonomously by observing random streams of facts from Wonderland. Alice is able to reason by means of computations that use bounded cognitive resources. Moreover, Alice develops her belief set by continuously forming, testing, and revising hypotheses. The system can learn a wide class of symbolic domains and challenge average human problem solvers in such domains as propositional logic and elementary arithmetic.


A description of living systems is still a topic of discussion among a number of disciplines. By an evaluation of the approaches, we get to an axis differentiating those that are indisputable in sense of dealing with verifiable and measurable phenomena. We thus also get to approaches that integrate particular extensions when dealing with the possibilities to describe living systems and processes. It is a task for biosemiotics to find connections of these approaches and thus ways to enrich each other or simply describe phenomena to the widest extent possible. One of the authors whose work is permeated by this idea is Howard Pattee. Inspired by his work, we discuss the options of description when talking about living systems and semiotic apparatuses. We do so by a formulation of two viewpoints that differ in questions of contextual dependency, interpretation and necessity of the existence of an autonomous agent as indispensable elements for the description of life phenomena.


Finding a balance between a centralised and decentralised curricular policy for general education and seeing teachers as autonomous agents of curriculum development is a recurrent issue in many countries. Radical reforms bring about the need to investigate whether and to what extent different parties – and first of all, teachers – are ready to accept and internalise the new policies and roles as curriculum leaders to ensure the sustainability of curriculum development. The purpose of this paper is to describe the development of a questionnaire for investigating Estonian teachers’ curricular work and preferences and to introduce the results of its piloting. The main topics covered by the questionnaire are teachers’ experience and autonomy in using and developing curricula, their preparation for curriculum development and preferences and expectations for the best curricular solutions. The developed questionnaire can be used for investigating teachers’ curricular work and preferences in different national contexts, thus enabling comparative studies across countries with different practices regarding curriculum policy.


In this article, it is described how the reconfigurable inter-operational buffers system built on the Digital Twin platform. Interoperating production buffers are now widely used in production. Their effect on the production system can be seen in decreasing downtime. From a cost-based point of view, the interoperating production buffers may generate a gain from the reduction in the volume of work-in-process, with which we increase production performance. This ratio depends on the average number of products that the buffers contain. The average number of pieces in the buffer is limited by the capacity of the buffer. The impact of turbulence in production is seen precisely on the average content of inter-operational production buffers. If we want to maintain work-in-process on optimal values, it is necessary to calculate and maintain the optimal capacity of each interoperating production buffer on the line. In the context of Smart Factory, it is currently possible that the current capacity of the interoperating production buffers is maintained according to the current state of production. In the subject system, real production facilities communicate with each other through the IoT as autonomous agents, which are decided on the basis of a formula to calculate the optimal capacity of the buffers, the prediction of faults and negotiation, thus actively maintaining the optimal capacity of intermediate operating production buffers for Smart Factory support.

:// . 4. Vinyals, M., Rodriguez-Aguilar, J.A., Cerquides, J. Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law (2011) Autonomous Agents and Multi-Agent Systems, 22 (3), pp. 439-464. 5. Mailler, Roger; Lesser, Victor (2004), “Solving Distributed Constraint Optimization Problems Using Cooperative Mediation”, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, IEEE Computer Society, pp. 438–445 6. D. Thain, T. Tannenbaum, M

References Alchourrón, C., Gärdenfors, P. and Makinson, D. (1985). On the logic of theory change: Partial meet contraction and revision functions, Journal of Symbolic Logic 50(2): 510-530. Amgoud, L., Dimopoulos, Y. and Moraitis, P. (2007). A unified and general framework for argumentation-based negotiation, 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), Honolulu, HI, USA, p. 158. Amgoud, L., Parsons, S. and Maudet, N. (2000). Arguments, dialogue and negotiation, inW. Horn (Ed.), Proceedings of the European Conference

emotions, Autonomous Agents and Multi-agent Systems   3 (3): 219-257. Gratch, J. and Marsella, S. (2004). Evaluating a computational model of emotion, Journal of Autonomous Agents and Multiagent Systems   11 (1): 23-43. Hansson, S. O. (1994). Decision theory: A brief introduction, Technical report , Royal Institute of Technology (KTH), Stockholm. Kahneman, D. (1973). Attention and Effort , Prentice Hall, Englewood Cliffs, NJ. Kowalczuk, Z. and Czubenko, M. (2009). Intelligent decision system of an autonomous robot, Seminar on Robot Autonomy, Poznań, Poland

References Wooldridge M., Jennings N. R., Kinny D. The Gaia methodology for agent-oriented analysis and design // Journal of Autonomous Agents and Multi-Agent Systems, 2000. - pp. 285-313. Padgham L., Winikoff M. Prometheus: A Methodology for Developing Intelligent Agents // Agent-Oriented Software Engineering III, LNCS, - Vol. 2585, Springer, 2003. - pp. 174-185. Burrafato P., Cossentino M. Designing a multi-agent solution for a bookstore with the PASSI methodology // Electronic Proceedings of Fourth International Bi-Conference Workshop on Agent

. - In: Proc. of International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2003), 2003, Melbourne, Australia, pp. 145-152. 6. Bazzan, A. L. C., F. Klügl. Case Studies on the Braess Paradox: Simulating Route Recommendation and Learning in Abstract and Microscopic Models. - Transportation Research, Part C. Emerging Technologies, Vol. 13, 2005, No 4, pp. 299-319. 7. Claes, R., T. Holvoet, D. Weyns. A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multi-Agent Systems. - IEEE Transactions on Intelligent Transportation Systems, Vol