Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions

A multi-agent partially observable Markov decision process (MPOMDP) is a modeling paradigm used for high-level planning of heterogeneous autonomous agents subject to uncertainty and partial observation. Despite their modeling efficiency, MPOMDPs have not received significant attention in safety-critical settings. In this paper, we use barrier functions to design policies for MPOMDPs that ensure safety. Notably, our method does not rely on discretization of the belief space, or finite memory. To this end, we formulate sufficient and necessary conditions for the safety of a given set based on discrete-time barrier functions (DTBFs) and we demonstrate that our formulation also allows for Boolean compositions of DTBFs for representing more complicated safe sets. We show that the proposed method can be implemented online by a sequence of one-step greedy algorithms as a standalone safe controller or as a safety-filter given a nominal planning policy. We illustrate the efficiency of the proposed methodology based on DTBFs using a high-fidelity simulation of heterogeneous robots.Comment: 8 pages and 4 figure

Control Theory Meets POMDPs: A Hybrid Systems Approach

Partially observable Markov decision processes(POMDPs) provide a modeling framework for a variety of sequential decision making under uncertainty scenarios in artificial intelligence (AI). Since the states are not directly observable ina POMDP, decision making has to be performed based on the output of a Bayesian filter (continuous beliefs); hence, making POMDPs intractable to solve and analyze. To overcome the complexity challenge of POMDPs, we apply techniques from control theory. Our contributions are fourfold: (i) We begin by casting the problem of analyzing a POMDP into analyzing the behavior of a discrete-time switched system. Then, (ii) in order to estimate the reachable belief space of a POMDP, i.e., the set of all possible evolutions given an initial belief distribution over the states and a set of actions and observations, we find over-approximations in terms of sub-level sets of Lyapunov-like functions. Furthermore, (iii) in order to verify safety and performance requirements of a given POMDP, we formulate a barrier certificate theorem

Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions

A multi-agent partially observable Markov decision process (MPOMDP) is a modeling paradigm used for high-level planning of heterogeneous autonomous agents subject to uncertainty and partial observation. Despite their modeling efficiency, MPOMDPs have not received significant attention in safety-critical settings. In this paper, we use barrier functions to design policies for MPOMDPs that ensure safety. Notably, our method does not rely on discretizations of the belief space, or finite memory. To this end, we formulate sufficient and necessary conditions for the safety of a given set based on discrete-time barrier functions (DTBFs) and we demonstrate that our formulation also allows for Boolean compositions of DTBFs for representing more complicated safe sets. We show that the proposed method can be implemented online by a sequence of one-step greedy algorithms as a standalone safe controller or as a safety-filter given a nominal planning policy. We illustrate the efficiency of the proposed methodology based on DTBFs using a high-fidelity simulation of heterogeneous robots

The Relationship Between Non-Completers\u27 Decisions to Leave Certificate Programs Before Earning Certificates and Their Career Goals

The primary purpose of this research was to conduct a systematic study that investigated ways other than awarding certificates of completion to determine the effectiveness of community colleges\u27 occupational-technical certificate programs. These programs are designed to provide students with the skills necessary to enter the workforce or receive promotions in existing employment, and they attract students whose primary goals are to gain the skills necessary to be more productive workers. This study was designed to assess the effectiveness of fifteen occupational-technical certificate programs at Tidewater Community College (TCC) according to the institution\u27s mission statement and to identify ways to encourage students to earn certificates of completion. Students\u27 academic records, responses to surveys, focus group discussions, and unsolicited comments were analyzed. These analyses determined that although TCC\u27s occupational-technical certificate programs do not meet accountability according to numbers of certificates awarded, they are meeting standards of effectiveness in respect to the college\u27s mission statement, providing students with skills necessary to successfully enter the workforce or advance in existing jobs. Thus, this study concluded that effectiveness of community colleges\u27 occupational-technical certificate programs could be measured by means other than traditional methods, such as counting graduates. Findings showed that 68% of the students who enrolled in TCC\u27s occupational-technical certificate programs for career goals made the decision to withdraw because they had met their goals. Significantly more students left programs for trade-related reasons after they had completed trade-related courses but before they enrolled in academic classes; significantly more students who were unemployed, while attending classes, withdrew from programs for trade-related influences than those from other employment groups. While non-completers perceived academic requirements as the primary barriers to students earning certificates, 56% of these former students did not believe institutional policies and procedures affected the decisions of students to leave programs. Seven areas of concern that signaled the need for further research are discussed. While three of these areas apply to TCC, four address concerns of community colleges in general

How Do Tor Users Interact With Onion Services?

Onion services are anonymous network services that are exposed over the Tor network. In contrast to conventional Internet services, onion services are private, generally not indexed by search engines, and use self-certifying domain names that are long and difficult for humans to read. In this paper, we study how people perceive, understand, and use onion services based on data from 17 semi-structured interviews and an online survey of 517 users. We find that users have an incomplete mental model of onion services, use these services for anonymity and have varying trust in onion services in general. Users also have difficulty discovering and tracking onion sites and authenticating them. Finally, users want technical improvements to onion services and better information on how to use them. Our findings suggest various improvements for the security and usability of Tor onion services, including ways to automatically detect phishing of onion services, more clear security indicators, and ways to manage onion domain names that are difficult to remember.Comment: Appeared in USENIX Security Symposium 201

Machine and social intelligent peer-assessment systems for assessing large student populations in massive open online education

The motivation of the European Etoile project is to create high quality free open education in complex systems science, including quality assured certification. Universities and colleges around the world are increasingly using online platforms to offer courses open to the public. Massive Open Online Courses or MOOCs give millions of people access to lectures delivered by prestigious universities. However, although some of these courses provide certification of attendance and completion, most do not provide any academic or professional recognition since this would imply a rigorous and complete evaluation of the student’s achievements. Since the number of students enrolled may exceed tens of thousands, it is impractical for a lecturer (or group of lecturers) to evaluate all students using conventional hand marking. Thus in order to be scalable, assessment must be automated. The state-of-the-art in automated assessment includes various methods and computerised tools including multiple choice questions, and intelligent marking techniques (involving complex semantic analysis). However, none of these completely cover the requirements needed for the implementation of an assessment system able to cope with very large populations of students and also able to guarantee the quality of evaluation required for higher education. The goal of this research is to propose, implement and evaluate a computer mediated social interaction system which can be applied to massive online learning communities. This must be a scalable system able to assess fairly and accurately student coursework and examinations. We call this approach “machine and socially intelligent peer assessment”. This paper describes our system and illustrates its application. Our approach combines the concepts of peer assessment and reputation systems to provide an independent computerised system which determines the degree and type of interaction between student peers based on a reputation score which emerges from the marking behaviour of each student and the interaction with other individuals of the community. A simulation experiment will be reported showing how reputation-based social structure can evolve in our peer marking system. A pilot experiment using a population of ninety 16-year old high school students in Colombia measured the marking accuracy of our system by comparing the statistical differences between the scores resulting from teacher marking (the ‘gold standard’), peer assessment using average scores, and our intelligent reputation-based peer assessment. This addresses the research question: to what extent does the proposed approach improve peer marking in terms of marking accuracy and fairness? We report the first results of this experiment, summarise the lessons learned, and describe further work