By Luc Steels, Rodney Brooks (Editors)
This quantity is the direct results of a convention during which a few prime researchers from the fields of man-made intelligence and biology amassed to envision even if there has been any floor to imagine new AI paradigm used to be forming itself and what the basic parts of this new paradigm have been. loads of scepsis is justified whilst researchers, quite within the cognitive sciences, speak about a brand new paradigm. Shifts in paradigm suggest not just new rules but in addition shifts in what constitutes strong difficulties, what counts accordingly, the experimental perform to validate effects, and the technological instruments had to do study. end result of the complexity of the subject material, paradigms abound within the cognitive sciences -- connectionism being the main renowned newcomer within the mid-1980s. This workshop team was once introduced jointly with the intention to make clear the typical floor, see what were completed thus far, and think about during which means the examine may flow extra. This quantity is a mirrored image of this significant assembly. It includes contributions that have been allotted ahead of the workshop yet then considerably broadened and revised to mirror the workshop discussions and newer technical paintings. Written in polemic shape, occasionally criticizing the paintings performed to date in the new paradigm, this assortment contains learn software descriptions, technical contributions, and place papers.
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Extra info for The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents
Sample text
1 Effectors of Reinforcement Learning Performance Despite the widely varying results of reinforcement learning in a variety of domains and circumstances, there is relatively little work explicitly investigating exactly what 40 2 Reinforcement Learning affects the performance of reinforcement learning with respect to domain characteristics, learning algorithms, and representations, as well as interactions between these components.
Recall that in reinforcement learning, the agent generally begins with no knowledge about the problem domain. In transfer and relational learning, knowledge that has been learned about one task is utilized to improve the learning process and efficiency in another, related task. A ‘related’ task can take different forms, some of which include sharing features (Konidaris et al. 2012), altering the allowable actions, altering the reward structure, or generalizing the applicability. This approach is also very similar to that of inductive learning from an artificial intelligence perspective (Michalski 1983), for which there has been some work that leverages this learning approach to develop agents that learn provably optimal solutions (Schmidhuber 2005, 2006).
Despite the success of this approach, the construction of the coding scheme requires a priori domain-specific knowledge and is not often straightforward (Ollington et al. 2009; Whiteson et al. 2011), and this approach becomes intractable with high-dimensional state spaces (Szepesvári 2010). A similar approach to tile coding is state aggregation in which similar states are grouped using a variety of methods, such as fuzzy clustering (Singh et al. 1995) or adaptive partitioning (Bonarini et al. 2007).