Integrated information theory (IIT) is a theoretical framework that provides
a quantitative measure to estimate when a physical system is conscious, its
degree of consciousness, and the complexity of the qualia space that the system
is experiencing. Formally, IIT rests on the assumption that if a surrogate
physical system can fully embed the phenomenological properties of
consciousness, then the system properties must be constrained by the properties
of the qualia being experienced. Following this assumption, IIT represents the
physical system as a network of interconnected elements that can be thought of
as a probabilistic causal graph, $\mathcal{G}$, where each node has an
input-output function and all the graph is encoded in a transition probability
matrix. Consequently, IIT's quantitative measure of consciousness, $\Phi$, is
computed with respect to the transition probability matrix and the present
state of the graph. In this paper, we provide a random search algorithm that is
able to optimize $\Phi$ in order to investigate, as the number of nodes
increases, the structure of the graphs that have higher $\Phi$. We also provide
arguments that show the difficulties of applying more complex black-box search
algorithms, such as Bayesian optimization or metaheuristics, in this particular
problem. Additionally, we suggest specific research lines for these techniques
to enhance the search algorithm that guarantees maximal $\Phi$.

本文介绍了一个随机搜索算法来优化意识的一个定量测量标准 phi，并给出了一些增强搜索算法的特定研究线路。

用先验引导随机搜索算法优化综合信息

Optimizing Integrated Information with a Prior Guided Random Search Algorithm

In this work, Transition Probability Matrix (TPM) is proposed as a new method
for extracting the features of nodes in the graph. The proposed method uses
random walks to capture the connectivity structure of a node's close
neighborhood. The information obtained from random walks is converted to
anonymous walks to extract the topological features of nodes. In the embedding
process of nodes, anonymous walks are used since they capture the topological
similarities of connectivities better than random walks. Therefore the obtained
embedding vectors have richer information about the underlying connectivity
structure. The method is applied to node classification and link prediction
tasks. The performance of the proposed algorithm is superior to the
state-of-the-art algorithms in the recent literature. Moreover, the extracted
information about the connectivity structure of similar networks is used to
link prediction and node classification tasks for a completely new graph.

介绍了一种基于过渡概率矩阵的新方法，使用随机游走捕获节点和其周围节点的连接结构，并将信息转换为匿名游走以提取节点的拓扑特征，并在节点嵌入过程中使用匿名游走来获得更好的性能，在节点分类和链接预测任务中，该算法的性能优于现有文献中的最新算法，同时还将类似网络的连接结构信息用于全新图的链接预测和节点分类任务中。