Embodiment and Its Influence on Informational Costs of Decision Density—Atomic Actions vs. Scripted Sequences

The importance of embodiment for effective robot performance has been postulated for a long time. Despite this, only relatively recently concrete quantitative models were put forward to characterize the advantages provided by a well-chosen embodiment. We here use one of these models, based on the concept of relevant information, to identify in a minimalistic scenario how and when embodiment affects the decision density. Concretely, we study how embodiment affects information costs when, instead of atomic actions, scripts are introduced, that is, predefined action sequences. Their inclusion can be treated as a straightforward extension of the basic action space. We will demonstrate the effect on informational decision cost of utilizing scripts vs. basic actions using a simple navigation task. Importantly, we will also employ a world with “mislabeled” actions, which we will call a “twisted” world. This is a model which had been used in an earlier study of the influence of embodiment on decision costs. It will turn out that twisted scenarios, as opposed to well-labeled (“embodied”) ones, are significantly more costly in terms of relevant information. This cost is further worsened when the agent is forced to lower the decision density by employing scripts (once a script is triggered, no decisions are taken until the script has run to its end). This adds to our understanding why well-embodied (interpreted in our model as well-labeled) agents should be preferable, in a quantifiable, objective sense.

Citizens, Infiltrators, and Others

This article will address the political objective behind the Modi government passing the Citizenship Amendment Act (CAA), planning to prepare a National Population Register (NPR) and a National Register of Citizens (NRC). It will examine the psychological impact of these moves on the minority community and why these moves were seen as precursors to their possible political disenfranchisement. It will examine the manner in which anti-CAA/NPR/NRC protest sites came up spontaneously across the country, how these protests were organized, and their lack of a clear leadership. It will also examine the decision of mainstream Opposition parties to keep away from these protests and the consequences of this decision. It will argue that this decision cost the political parties and the nation dearly. It shifted the political discourse within the majoritarian spectrum and led to these parties losing credibility with the protestors and the minority community. Finally, it will also examine the failure of the mainstream political parties in preventing violence by the state against the protestors (such as in Uttar Pradesh, Karnataka and Delhi) and in moderating the protests so that they do not fall prey to violent provocation.

Intuitionistic Fuzzy Three-Way Decision Model Based on the Three-Way Granular Computing Method

Three-way decisions, as a general model for uncertain information processing and decisions, mainly utilize the threshold generated by the decision cost matrix to determine the decision category of the object. However, the determination of the threshold is usually accompanied by varying degrees of subjectivity. In addition, the potential symmetrical relationship between the advantages and disadvantages of the decision cost is also a problem worthy of attention. In this study, we propose a novel intuitionistic fuzzy three-way decision (IFTWD) model based on a three-way granular computing method. First, we present the calculation methods for the possibility of membership state and non-membership state, as well as prove the related properties. Furthermore, we investigate the object information granules, i.e., the fine-grained, medium-grained, and coarse-grained objects, by combining the state probability distribution and probability distribution. Then, for decision and evaluation issues, we define the superiority-compatibility relation and inferiority-compatibility relation for IFTWD model construction. In addition, we use the superiority degree and inferiority degree instead of the original thresholds and design a new method for evaluating decision cost. Finally, we focus on the algorithm research of the proposed model and present an empirical study of agricultural ecological investment in Hubei Province to demonstrate the effectiveness of our model.

Dissecting the neurofunctional bases of intentional action

Here we challenge and present evidence that expands the what, when, and whether anatomical model of intentional action, which states that internally driven decisions about the content and timing of our actions and about whether to act at all depend on separable neural systems, anatomically segregated along the medial wall of the frontal lobe. In our fMRI event-related paradigm, subjects acted following conditional cues or following their intentions. The content of the actions, their timing, or their very occurrence were the variables investigated, together with the modulating factor of intentionality. Besides a shared activation of the pre-supplementary motor area (pre-SMA) and anterior cingulate cortex (ACC) for all components and the SMA proper for the when component, we found specific activations beyond the mesial prefrontal wall involving the parietal cortex for the what component or subcortical gray structures for the when component. Moreover, we found behavioral, functional, anatomical, and brain connectivity evidence that the self-driven decisions on whether to act require a higher interhemispheric cooperation: This was indexed by a specific activation of the corpus callosum whereby the less the callosal activation, the greater was the decision cost at the time of the action in the whether trials. Furthermore, tractography confirmed that the fibers passing through the callosal focus of activation connect the two sides of the frontal lobes involved in intentional trials. This is evidence of non-unitary neural foundations for the processes involved in intentional actions with the pre-SMA/ACC operating as an intentional hub. These findings may guide the exploration of specific instances of disturbed intentionality.

Local view based cost-sensitive attribute reduction

In traditional cost-sensitive attribute reduction, the variation of decision cost is referred to as a global difference of costs because the considered decision cost is the variation of sum of decision costs over all objects. However, such reduction does not take the variation of decision costs of each object into account. To solve this problem, a local view based cost-sensitive attribute reduction is introduced. Firstly, through considering the variation of decision costs of single object if the used attributes change, a local difference of costs is presented. Secondly, on the basis of the fuzzy decision-theoretic rough set model, a new significance function is given to measure the importance of attribute. Finally, the experimental results illustrate that by comparing the traditional reduction, the proposed local view can decreases both global and local differences of costs effectively on several UCI data sets.

δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions

Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, aδ-cut decision-theoretic rough set is proposed, which is based on theδ-cut quantitative indiscernibility relation. Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively. The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities. This study suggests potential application areas and new research trends concerning rough set theory.

Cost-Sensitive Attribute Reduction in Decision-Theoretic Rough Set Models

In recent years, the theory of decision-theoretic rough set and its applications have been studied, including the attribute reduction problem. However, most researchers only focus on decision cost instead of test cost. In this paper, we study the attribute reduction problem with both types of costs in decision-theoretic rough set models. A new definition of attribute reduct is given, and the attribute reduction is formulated as an optimization problem, which aims to minimize the total cost of classification. Then both backtracking and heuristic algorithms to the new problem are proposed. The algorithms are tested on four UCI (University of California, Irvine) datasets. Experimental results manifest the efficiency and the effectiveness of both algorithms. This study provides a new insight into the attribute reduction problem in decision-theoretic rough set models.