The Green Choice: Learning and Influencing Human Decisions on Shared Roads

Autonomous vehicles have the potential to increase the capacity of roads via platooning, even when human drivers and autonomous vehicles share roads. However, when users of a road network choose their routes selfishly, the resulting traffic configuration may be very inefficient. Because of this, we consider how to influence human decisions so as to decrease congestion on these roads. We consider a network of parallel roads with two modes of transportation: (i) human drivers who will choose the quickest route available to them, and (ii) ride hailing service which provides an array of autonomous vehicle ride options, each with different prices, to users. In this work, we seek to design these prices so that when autonomous service users choose from these options and human drivers selfishly choose their resulting routes, road usage is maximized and transit delay is minimized. To do so, we formalize a model of how autonomous service users make choices between routes with different price/delay values. Developing a preference-based algorithm to learn the preferences of the users, and using a vehicle flow model related to the Fundamental Diagram of Traffic, we formulate a planning optimization to maximize a social objective and demonstrate the benefit of the proposed routing and learning scheme.Comment: Submitted to CDC 201

Robust Subspace System Identification via Weighted Nuclear Norm Optimization

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.Comment: Submitted to the IFAC World Congress 201

What is the expected human childhood? Insights from evolutionary anthropology

In psychological research, there are often assumptions about the conditions that children expect to encounter during their development. These assumptions shape prevailing ideas about the experiences that children are capable of adjusting to, and whether their responses are viewed as impairments or adaptations. Specifically, the expected childhood is often depicted as nurturing and safe, and characterized by high levels of caregiver investment. Here, we synthesize evidence from history, anthropology, and primatology to challenge this view. We integrate the findings of systematic reviews, meta-analyses, and cross-cultural investigations on three forms of threat (infanticide, violent conflict, and predation) and three forms of deprivation (social, cognitive, and nutritional) that children have faced throughout human evolution. Our results show that mean levels of threat and deprivation were higher than is typical in industrialized societies, and that our species has experienced much variation in the levels of these adversities across space and time. These conditions likely favored a high degree of phenotypic plasticity, or the ability to tailor development to different conditions. This body of evidence has implications for recognizing developmental adaptations to adversity, for cultural variation in responses to adverse experiences, and for definitions of adversity and deprivation as deviation. from the expected human childhood

A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications

We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one

Diagnosis and Repair for Synthesis from Signal Temporal Logic Specifications

We address the problem of diagnosing and repairing specifications for hybrid systems formalized in signal temporal logic (STL). Our focus is on the setting of automatic synthesis of controllers in a model predictive control (MPC) framework. We build on recent approaches that reduce the controller synthesis problem to solving one or more mixed integer linear programs (MILPs), where infeasibility of a MILP usually indicates unrealizability of the controller synthesis problem. Given an infeasible STL synthesis problem, we present algorithms that provide feedback on the reasons for unrealizability, and suggestions for making it realizable. Our algorithms are sound and complete, i.e., they provide a correct diagnosis, and always terminate with a non-trivial specification that is feasible using the chosen synthesis method, when such a solution exists. We demonstrate the effectiveness of our approach on the synthesis of controllers for various cyber-physical systems, including an autonomous driving application and an aircraft electric power system

Decline of vasopressin immunoreactivity and messenger RNA levels in the bed nucleus of the stria terminalis following castration

Vasopressinergic (VP) neurons in the bed nucleus of the stria terminalis (BNST) of the rat are regulated by gonadal steroids. Gonadectomy causes the projections of the BNST to lose their VP immunoreactivity gradually over a period lasting more than 2 months. Here we have compared the rate of decline of VP mRNA and VP immunoreactivity in the BNST of adult male rats following castration. In experiment 1, the peak number of VP-immunoreactive cells and the level of VP gene expression were compared in sham-operated controls and at 1, 3, or 8 weeks postcastration. The number of VP-immunoreactive cells was not decreased at 1 week postcastration but was significantly reduced (p \u3c 0.0001) at 3 and 8 weeks postcastration. VP gene expression declined more rapidly, and both the total number of labeled cells (p \u3c 0.0001) and the average number of grains per cell (p \u3c 0.01) were significantly reduced by 1 week postcastration. No VP-expressing cells were detectable at 3 or 8 weeks. The difference in the rate of decline in the number of cells labeled by the two techniques following castration did not appear to be due to colchicine pretreatment. In experiment 2, VP mRNA in the BNST was compared in sham-operated controls or at 1, 3, or 7 d postcastration. A significant decrease (p \u3c 0.01) in the average number of grains per cell was detectable by just 1 d following castration, and the number of labeled cells was significantly reduced (p \u3c 0.001) by 3 d postcastration. These results indicate that the capacity of BNST cells to synthesize VP responds more dynamically to changes in gonadal steroid levels than do levels of VP immunoreactivity. This difference may reflect the delay between VP gene expression and the processing of VP precursor molecules. Alternatively, gonadal steroids may modulate the release of VP from cells in the BNST

Reactive Synthesis from Signal Temporal Logic Specifications

We present a counterexample-guided inductive synthesis approach to controller synthesis for cyber-physical systems subject to signal temporal logic (STL) specifications, operating in potentially adversarial nondeterministic environments. We encode STL specifications as mixed integer-linear constraints on the variables of a discrete-time model of the system and environment dynamics, and solve a series of optimization problems to yield a satisfying control sequence. We demonstrate how the scheme can be used in a receding horizon fashion to fulfill properties over unbounded horizons, and present experimental results for reactive controller synthesis for case studies in building climate control and autonomous driving