Get Artificial General Intelligence: 8th International PDF

By Jordi Bieger, Ben Goertzel, Alexey Potapov

ISBN-10: 3319213644

ISBN-13: 9783319213644

ISBN-10: 3319213652

ISBN-13: 9783319213651

This booklet constitutes the refereed court cases of the eighth foreign convention on synthetic common Intelligence, AGI 2015, held in Berlin, Germany in July 2015. The forty-one papers have been conscientiously reviewed and chosen from seventy two submissions. The AGI convention sequence has performed and keeps to play, an important function during this resurgence of study on synthetic intelligence within the deeper, unique experience of the time period of “artificial intelligence”. The meetings inspire interdisciplinary examine in line with assorted understandings of intelligence and exploring varied techniques. AGI study differs from the normal AI examine through stressing at the versatility and wholeness of intelligence and by way of engaging in the engineering perform in keeping with an summary of a procedure such as the human brain in a undeniable sense.

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Additional info for Artificial General Intelligence: 8th International Conference, AGI 2015, AGI 2015, Berlin, Germany, July 22-25, 2015, Proceedings

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AI] 2. : Church: a language for generative models (2008). PL] 3. 4. Microsoft Research Camb. (2010). com/infernet 4. : Effective Bayesian inference for stochastic programs. Proc. National Conference on Artificial Intelligence (AAAI), pp. 740–747 (1997) 5. : A dynamic programming algorithm for inference in recursive probabilistic programs (2012). AI] 6. : General-purpose MCMC inference over relational structures. In: Proc. 22nd Conference on Uncertainty in Artificial Intelligence, pp. 349–358 (2006) 7.

A tree with one root task which is decomposed into a sequence of sub-tasks. 4 3D Scene Reconstruction This subsystems performs the conversion of 2D sensor data into a 3D scene using an iterative algorithm depicted below. Fig. 5. 3D Scene Reconstruction: 1) Perform classical 2D feature extraction (edges, textures, …) and retrieve episodic memory (EM) object matches. 2) Check the EM for scenes that contain all or part of the objects. 3) Construct a plausible scene with expectations from higher SBR levels.

Keywords: Cognitive architecture · Self-model · Meta-reasoning · Description logics · Introspection · Multi-agent · Plan reasoning 1 Introduction In this paper we describe Scene Based Reasoning (SBR), a cognitive architecture in the tradition of SOAR [14], ACT-R [1] and similar systems [10]. Particular similarities exist with the ICARUS system [16] with respect to the explicit representation of plans with decompositions, the “grounding in physical states”, the purpose of controlling a physical agent, the use of observable attributes as a semantic base and spatial roles/ relationships between objects.

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Artificial General Intelligence: 8th International Conference, AGI 2015, AGI 2015, Berlin, Germany, July 22-25, 2015, Proceedings by Jordi Bieger, Ben Goertzel, Alexey Potapov

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