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Similarly to individual learning in a classroom, a procedure could be applied to collective learning, namely to social procedures among humans who are starting to create institutional networks for combating global climate change – one of the most urgent tasks at present.

This chapter describes how the social processes emerging in a five-level web-based negotiation game may be conceived in such a way that these form a sequence of growing and decaying intensity in various modes of social interaction. The results show that the recognizer using IRL can recognize the destination effectively even if the intention changes during the midway, and it performs better than other models in terms of several most frequently-used metrics.įor university teaching in general, and specifically for the transdisciplinary curriculum of “Environmental Systems Analysis”, web-based learning procedures provide excellent opportunities for socially induced understanding and consensus building.

In experiments, we build the game scenario in the Unreal Engine 4 environment and collect the moving trajectories from the human players in several different tasks for evaluating the performance of our methods. To adapt to the dynamic environment, the Maximum Entropy Inverse Reinforcement Learning (Ma圎nt IRL) method is transformed by defining a Fitness index to measure the effect of weight and use the Nelder-Mead polyhedron search to find the optimal weight. In this paper, the Inverse Reinforcement Learning (IRL) method is adopted in opponent behavior learning for the destination recognition problem. Besides, most of the current works do not establish a behavioral model of the identified person, and there is almost no work modeling individual behavioral preference, which limits the accuracy of the recognition results. To solve this problem, a semi-Markov decision model (SMDM), which explicitly models the duration of an action, is proposed in this paper. However, the common problem in these frameworks is that they usually lack of the modeling of the action duration as in real-world scenarios the agent may take several steps to transit between grids. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-the-shelf planners.

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