Peter J. Jansen

Transnational Information Sharing, Event Notification, Rule Enforcement and Process Coordination

Solutions to global problems such as disease detection and control, terrorism, immigration and border control, and illicit drug trafficking require sharing and coordinating information and collaboration among government agencies within a country and across national boundaries. This paper presents an approach to achieve information sharing, event notification, enforcement of policies, constraints, regulations, security and privacy rules, and process coordination. The proposed system, designed in collaboration with stakeholders and end users in two Latin American countries, achieves the desired capabilities by integrating a distributed query processor (DQP) that provides form-based and conversational user interfaces, a language translation system, an event server for event filtering and notification, and an event-trigger-rule server. The Web-services infrastructure is used to achieve the interoperation of these heterogeneous component systems. A prototype of the integrated transnational information system is described.

Problematic Positions and Speculative Play

When commentators annotate chess games, they make use of various symbols to show their evaluation of moves or positions. For example, one might ind something like 23. Rel? -+, indicating that white’s 23rd move was bad (‘?’) and the position is now lost (‘-+’). Whereas ‘?’s can have game-theoretic significance, ‘!’s (“very good moves”) and ‘!!’s (“excellent moves”) do not (since it is not possible to turn a game-theoretically lost position into a draw or win). The meaning of a ‘!’s (and other evaluative symbols) must therefore be found, not in a game-theoretic analysis of the game, but rather in its “psychological” aspects.

Proactive Skill Posting in Referral Networks

Distributed learning in expert referral networks is an emerging challenge in the intersection of Active Learning and Multi-Agent Reinforcement Learning, where experts—humans or automated agents—can either solve problems themselves or refer said problems to others with more appropriate expertise. Recent work demonstrated methods that can substantially improve the overall performance of a network and proposed a distributed referral-learning algorithm, DIEL (Distributed Interval Estimation Learning), for learning appropriate referral choices. This paper augments the learning setting with a proactive skill posting step where experts can report some of their top skills to their colleagues. We found that in this new learning setting with meaningful priors, a modified algorithm, proactive-DIEL, performed initially much better and reached its maximum performance sooner than DIEL on the same data set used previously. Empirical evaluations show that the learning algorithm is robust to random noise in an expert’s estimation of her own expertise, and there is little advantage in misreporting skills when the rest of the experts report truthfully, i.e., the algorithm is near Bayesian-Nash incentive-compatible.

Symmetric Probabilistic Alignment

We recently decided to develop a new alignment algorithm for the purpose of improving our Example-Based Machine Translation (EBMT) systems performance, since subsentential alignment is critical in locating the correct translation for a matched fragment of the input. Unlike most algorithms in the literature, this new Symmetric Probabilistic Alignment (SPA) algorithm treats the source and target languages in a symmetric fashion. In this short paper, we outline our basic algorithm and some extensions for using context and positional information, and compare its alignment accuracy on the Romanian-English data for the shared task with IBM Model 4 and the reported results from the prior workshop.

Robust learning in expert networks: a comparative analysis

Human experts as well as autonomous agents in a referral network must decide whether to accept a task or refer to a more appropriate expert, and if so to whom. In order for the referral network to improve over time, the experts must learn to estimate the topical expertise of other experts. This article extends concepts from Multi-agent Reinforcement Learning and Active Learning to referral networks for distributed learning in referral networks. Among a wide array of algorithms evaluated, Distributed Interval Estimation Learning (DIEL), based on Interval Estimation Learning, was found to be superior for learning appropriate referral choices, compared to 𝜖-Greedy, Q-learning, Thompson Sampling and Upper Confidence Bound (UCB) methods. In addition to a synthetic data set, we compare the performance of the stronger learning-to-refer algorithms on a referral network of high-performance Stochastic Local Search (SLS) SAT solvers where expertise does not obey any known parameterized distribution. An evaluation of overall network performance and a robustness analysis is conducted across the learning algorithms, with an emphasis on capacity constraints and evolving networks, where experts with known expertise drop off and new experts of unknown performance enter — situations that arise in real-world scenarios but were heretofore ignored.

Robust Learning in Expert Networks: A Comparative Analysis

Learning how to refer effectively in an expert-referral network is an emerging challenge at the intersection of Active Learning and Multi-Agent Reinforcement Learning. Distributed interval estimation learning (DIEL) was previously found to be promising for learning appropriate referral choices, compared to greedy and Q-learning methods. This paper extends these results in several directions: First, learning methods with several multi-armed bandit (MAB) algorithms are compared along with greedy variants, each optimized individually. Second, DIEL’s rapid performance gain in the early phase of learning proved equally convincing in the case of multi-hop referral, a condition not heretofore explored. Third, a robustness analysis across the learning algorithms, with an emphasis on capacity constraints and evolving networks (experts dropping out and new experts of unknown performance entering) shows rapid recovery. Fourth, the referral paradigm is successfully extended to teams of Stochastic Local Search (SLS) SAT solvers with different capabilities.

Proactive- DIEL in Evolving Referral Networks

Distributed learning in expert referral networks is a new Active Learning paradigm where experts—humans or automated agents—solve problems if they can or refer said problems to others with more appropriate expertise. Recent work augmented the basic learning-to-refer method with proactive skill posting, where experts may report their top skills to their colleagues, and proposed a modified algorithm, proactive-DIEL (Distributed Interval Estimation Learning), that takes advantage of such one-time posting instead of using an uninformed prior. This work extends the method in three main directions: (1) Proactive-DIEL is shown to work on a referral network of automated agents, namely SAT solvers, (2) Proactive-DIEL’s reward mechanism is extended to another referral-learning algorithm, \(\epsilon \)-Greedy, with some appropriate modifications. (3) The method is shown robust with respect to evolving networks where experts join or drop off, requiring the learning method to recover referral expertise. In all cases the proposed method exhibits superiority to the state of the art.