Ghana coastal fisheries governance dialogue: developing options for a legal framework for fisheries co-management in Ghana

The Third National Fisheries Governance Dialogue was a direct follow up on the Second National Fisheries Governance Dialogue held in Elmina in April 2012. It was agreed at the Second dialogue that co-management was the way forward for sustaining Ghana’s fisheries and that its success would depend on a supportive legal framework. The two day dialogue meeting consisted of four key presentations focusing on: the current status of fisheries in Ghana; co-management as a fresh approach to fisheries; outcomes from the regional stakeholder consultations on co-management structure; and outcomes from the research on the legal framework. The presentations were followed by four breakout groups that generated ideas for co-management structures for different species namely pelagic fish or Sardinella, near shore demersal, Volta lake, and lagoons and estuaries. Key elements for co-management structures and elements of a co-management legal framework were later identified during plenary discussions.

Dialogue to address the roots of resource competition: Lessons for policy and practice

Conflict management is an intrinsic element of natural resource management, and becomes increasingly important amid growing pressure on natural resources from local uses, as well as from external drivers such as climate change and international investment. If policymakers and practitioners aim to truly improve livelihood resilience and reduce vulnerabilities of poor rural households, issues of resource competition and conflict management cannot be ignored. This synthesis report summarizes outcomes and lessons from three ecoregions: Lake Victoria, with a focus on Uganda; Lake Kariba, with a focus on Zambia; and Tonle Sap Lake in Cambodia. Partners used a common approach to stakeholder engagement and action research that we call “Collaborating for Resilience”. In each region, partners assisted local stakeholders in developing a shared understanding of risks and opportunities, weighing alternative actions, developing action plans, and evaluating and learning from the outcomes. These experiences demonstrate that investing in capacities for conflict management is practical and can contribute to broader improvements in resource governance.

Enhancing capacities of fishing communities: ICSF-BOBLME sub-regional dialogue on labour, migration and fisheries management. Report of the proceedings, 11 to 13 December 2013, Chulalongkorn University,Thailand

This report on the “Sub-regional Dialogue on Labour, Migration and Fisheries Management”, held at Chulalongkorn University, Bangkok, Thailand, from 11 to 13 December 2013, highlights the issue of migrant labour on board fishing vessels and the problems migrant workers face in their workaday lives. This report will be useful for students, researchers, activists and anyone else interested in matters related to fisheries and small-scale fishing communities.

The design and implementation of dialogue control in voice operated database inquiry systems

This paper describes work performed as part of the U.K. Alvey sponsored Voice Operated Database Inquiry System (VODIS) project in the area of intelligent dialogue control. The principal aims of the work were to develop a habitable interface for the untrained user; to investigate the degree to which dialogue control can be used to compensate for deficiencies in recognition performance; and to examine the requirements on dialogue control for generating natural speech output. A data-driven methodology is described based on the use of frames in which dialogue topics are organized hierarchically. The concept of a dynamically adjustable scope is introduced to permit adaptation to recognizer performance and the use of historical and hierarchical contexts are described to facilitate the construction of contextually relevant output messages. © 1989.

Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

Technology management - Structuring the strategic dialogue

Successful innovation requires effective communication within and between technical and nontechnical communities, which can be challenging due to different educational backgrounds, experience, perceptions, and attitudes. Roadmapping has emerged as a method that can enable effective dialogue between these groups, and the way in which information is structured is a key feature that enables this communication. This is an area that has not received much attention in the literature, and this article seeks to address this gap by describing in detail the structures that have been successfully applied in roadmapping workshops and processes, from which key learning points and future research directions are identified.

Bayesian dialogue system for the let's go spoken dialogue challenge

This paper describes how Bayesian updates of dialogue state can be used to build a bus information spoken dialogue system. The resulting system was deployed as part of the 2010 Spoken Dialogue Challenge. The purpose of this paper is to describe the system, and provide both simulated and human evaluations of its performance. In control tests by human users, the success rate of the system was 24.5% higher than the baseline Lets Go! system. ©2010 IEEE.

Parameter learning for POMDP spoken dialogue models

The partially observable Markov decision process (POMDP) provides a popular framework for modelling spoken dialogue. This paper describes how the expectation propagation algorithm (EP) can be used to learn the parameters of the POMDP user model. Various special probability factors applicable to this task are presented, which allow the parameters be to learned when the structure of the dialogue is complex. No annotations, neither the true dialogue state nor the true semantics of user utterances, are required. Parameters optimised using the proposed techniques are shown to improve the performance of both offline transcription experiments as well as simulated dialogue management performance. ©2010 IEEE.

Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager

Effective dialogue management is critically dependent on the information that is encoded in the dialogue state. In order to deploy reinforcement learning for policy optimization, dialogue must be modeled as a Markov Decision Process. This requires that the dialogue statemust encode all relevent information obtained during the dialogue prior to that state. This can be achieved by combining the user goal, the dialogue history, and the last user action to form the dialogue state. In addition, to gain robustness to input errors, dialogue must be modeled as a Partially Observable Markov Decision Process (POMDP) and hence, a distribution over all possible states must be maintained at every dialogue turn. This poses a potential computational limitation since there can be a very large number of dialogue states. The Hidden Information State model provides a principled way of ensuring tractability in a POMDP-based dialogue model. The key feature of this model is the grouping of user goals into partitions that are dynamically built during the dialogue. In this article, we extend this model further to incorporate the notion of complements. This allows for a more complex user goal to be represented, and it enables an effective pruning technique to be implemented that preserves the overall system performance within a limited computational resource more effectively than existing approaches. © 2011 ACM.

Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as POMDPs

This article presents a novel algorithm for learning parameters in statistical dialogue systems which are modeled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy that selects the system's responses based on the inferred state; and a reward function that specifies the desired behavior of the system. Ideally both the model parameters and the policy would be designed to maximize the cumulative reward. However, while there are many techniques available for learning the optimal policy, no good ways of learning the optimal model parameters that scale to real-world dialogue systems have been found yet. The presented algorithm, called the Natural Actor and Belief Critic (NABC), is a policy gradient method that offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected cumulative reward. The resulting gradient is then used to adapt both the prior distribution of the dialogue model parameters and the policy parameters. In addition, the article presents a variant of the NABC algorithm, called the Natural Belief Critic (NBC), which assumes that the policy is fixed and only the model parameters need to be estimated. The algorithms are evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximize the expected cumulative reward result in significantly improved performance compared to the baseline hand-crafted model parameters. The algorithms are also compared to optimization techniques using plain gradients and state-of-the-art random search algorithms. In all cases, the algorithms based on the natural gradient work significantly better. © 2011 ACM.