Non-adversarial advocates and gatekeepers : lawyers, FDR practitioners and co-operative post-separation parenting

The compulsory dispute resolution requirements in family law parenting cases create new roles and obligations for both lawyers and family dispute resolution (FDR) practitioners. This article will discuss how the legislative provisions impact on both sets of professionals in practice. It will also highlight the increased non-adversarial role of lawyers and a new role for FDR practitioners as “gatekeepers” to family courts in cases requiring FDR certificates.

Optimal online prediction in adversarial environments

In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

Optimal online prediction in adversarial environments

In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

Collaborative intrusion detection framework : characteristics, adversarial opportunities and countermeasures

Complex Internet attacks may come from multiple sources, and target multiple networks and technologies. Nevertheless, Collaborative Intrusion Detection Systems (CIDS) emerges as a promising solution by using information from multiple sources to gain a better understanding of objective and impact of complex Internet attacks. CIDS also help to cope with classical problems of Intrusion Detection Systems (IDS) such as zero-day attacks, high false alarm rates and architectural challenges, e. g., centralized designs exposing the Single-Point-of-Failure. Improved complexity on the other hand gives raise to new exploitation opportunities for adversaries. The contribution of this paper is twofold. We first investigate related research on CIDS to identify the common building blocks and to understand vulnerabilities of the Collaborative Intrusion Detection Framework (CIDF). Second, we focus on the problem of anonymity preservation in a decentralized intrusion detection related message exchange scheme. We use techniques from design theory to provide multi-path peer-to-peer communication scheme where the adversary can not perform better than guessing randomly the originator of an alert message.

Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Mainstreaming therapeutic jurisprudence and the adversarial paradigm—incommensurability and the possibility of a shared disciplinary matrix

Problem-solving courts appear to achieve outcomes that are not common in mainstream courts. There are increasing calls for the adoption of more therapeutic and problem-solving practices by mainstream judges in civil and criminal courts in a number of jurisdictions, most notably in the United States and Australia. Currently, a judge who sets out to exercise a significant therapeutic function is likely to be doing so in a specialist court or jurisdiction, outside the mainstream court system, and arguably, outside the adversarial paradigm itself. To some extent, this work is tolerated but marginalised. However, do therapeutic and problem-solving functions have the potential to help define, rather than simply complement, the role of judicial officers? The core question addressed in this thesis is whether the judicial role could evolve to be not just less adversarial, but fundamentally non-adversarial. In other words, could we see—or are we seeing—a juristic paradigm shift not just in the colloquial, casual sense of the word, but in the strong, worldview changing sense meant by Thomas Kuhn? This thesis examines the current relationship between adversarialism and therapeutic jurisprudence in the context of Kuhn’s conception of the transition from periods of ‘normal science’, through periods of anomaly and disciplinary crises to paradigm shifts. It considers whether therapeutic jurisprudence and adversarialism are incommensurable in the Kuhnian sense, and if so, what this means for the relationship between the two, and for the agenda to mainstream therapeutic jurisprudence. The thesis asserts that Kuhnian incommensurability is, in fact, a characteristic of the relationship between adversarialism and therapeutic jurisprudence, but that the possibility of a therapeutic paradigm shift in law can be reconciled with many adversarial and due process principles by relating this incommensurability to a broader disciplinary matrix.

Open problem : adversarial multiarmed bandits with limited advice

Adversarial multiarmed bandits with expert advice is one of the fundamental problems in studying the exploration-exploitation trade-o. It is known that if we observe the advice of all experts on every round we can achieve O(√KTlnN) regret, where K is the number of arms, T is the number of game rounds, and N is the number of experts. It is also known that if we observe the advice of just one expert on every round, we can achieve regret of order O(√NT). Our open problem is what can be achieved by asking M experts on every round, where 1 < M < N.

Representing clients from courtroom to mediation settings : switching hats between adversarial advocacy and dispute resolution advocacy

In the vast majority of cases legal representation in mediation can provide many advantages for clients. However, in some, progress can be thwarted when lawyers do not understand the goals of the mediation process and their dispute resolution advocacy role. This article will explore some of the similarities and differences between the knowledge and skills that lawyers can draw upon when representing clients in adversarial court hearings as compared with non-adversarial settings, such as in mediations. One key distinction is the different approaches that legal representatives can use to effectively act in the best interests of clients. This article will highlight how an appreciation of such distinctions can assist lawyers to “switch” hats between their adversarial and non-adversarial roles. In particular, an understanding that the duty to promote the best interests of clients in mediation is consistent with a collaborative and problem-solving approach can greatly assist in the resolution process.

One practical algorithm for both stochastic and adversarial bandits

We present an algorithm for multiarmed bandits that achieves almost optimal performance in both stochastic and adversarial regimes without prior knowledge about the nature of the environment. Our algorithm is based on augmentation of the EXP3 algorithm with a new control lever in the form of exploration parameters that are tailored individually for each arm. The algorithm simultaneously applies the “old” control lever, the learning rate, to control the regret in the adversarial regime and the new control lever to detect and exploit gaps between the arm losses. This secures problem-dependent “logarithmic” regret when gaps are present without compromising on the worst-case performance guarantee in the adversarial regime. We show that the algorithm can exploit both the usual expected gaps between the arm losses in the stochastic regime and deterministic gaps between the arm losses in the adversarial regime. The algorithm retains “logarithmic” regret guarantee in the stochastic regime even when some observations are contaminated by an adversary, as long as on average the contamination does not reduce the gap by more than a half. Our results for the stochastic regime are supported by experimental validation.

Tracking adversarial targets

We study linear control problems with quadratic losses and adversarially chosen tracking targets. We present an efficient algorithm for this problem and show that, under standard conditions on the linear system, its regret with respect to an optimal linear policy grows as O(log^2 T), where T is the number of rounds of the game. We also study a problem with adversarially chosen transition dynamics; we present an exponentiallyweighted average algorithm for this problem, and we give regret bounds that grow as O(sqtr p T).