A Novel Multi-Agent Planning System for Digital Interactive Storytelling

Digital Interactive Storytelling (DIS) is a relatively novel area of computer entertainment that aims at investigating interactive applications capable of generating consistent, emergent, and rich stories. To provide new solutions for DIS, we designed and are implementing and evaluating a novel multi-agent DIS framework, DIEGESIS, which includes agents' coordination and new planning and re-planning solutions. In this article, we discuss the design and implementation of DIEGESIS, explaining in detail the mechanisms of our planning algorithms, and the story execution and agent coordination algorithms, along with a planning methods evaluation and agent planning and coordination examples. We are currently in the process of creating a large DIS scenario, involving the story of Homer's Troy, with several levels that will allow us to further evaluate and expand our system

DIEGESIS A multi-agent Digital Interactive Storytelling framework using planning and re-planning techniques

In recent years, the field of Digital Interactive Storytelling (DIS) has become very popular both in academic circles, as well as in the gaming industry, in which stories are becoming a unique selling point. Academic research on DIS focuses in the search for techniques that allow the creation of systems that can generate dynamically interesting stories which are not linear and can change dynamically at runtime as a consequence of a player’s actions, therefore leading to different story endings.To reach this goal, DIS systems usually employ Artificial Intelligence planning and re-planning algorithms as part of their solution. There is a lack of algorithms created specifically for DIS purposes since most DIS systems use generic algorithms, and they do not usually assess if and why a given algorithm is the best solution for their purposes. Additionally, there is no unified way (e.g. in the form of a selection of metrics) to evaluate such systems and algorithms.To address these issues and to provide new solutions to the DIS field, we performed a review of related DIS systems and algorithms, and based on the critical analysis of that work we designed and implemented a novel multi-agent DIS framework called DIEGESIS, which includes –among other novel aspects- two new DIS-focused planning and re-planning algorithms.To ensure that our framework and its algorithms have met the specifications we set, we created a large scale evaluation scenario which models the story of Troy, derived from Homer’s epic poem, “Iliad”, which we used to perform a number of evaluations based on metrics that we chose and we consider valuable for the DIS field. This collection of requirements and evaluations could be used in the future from other DIS systems as a unified test-bed for analysis and evaluation of such systems

Parallelizing RRT on large-scale distributed-memory architectures

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance

Evolving a Behavioral Repertoire for a Walking Robot

Numerous algorithms have been proposed to allow legged robots to learn to walk. However, the vast majority of these algorithms is devised to learn to walk in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which com-bines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of con-trollers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.Comment: 33 pages; Evolutionary Computation Journal 201

Optimising the management of vaginal discharge syndrome in Bulgaria: cost effectiveness of four clinical algorithms with risk assessment

OBJECTIVES: To evaluate the performance and cost effectiveness of the WHO recommendations of incorporating risk-assessment scores and population prevalence of Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) into vaginal discharge syndrome (VDS) algorithms. METHODS: Non-pregnant women presenting with VDS were recruited at a non-governmental sexual health clinic in Sofia, Bulgaria. NG and CT were diagnosed by PCR and vaginal infections by microscopy. Risk factors for NG/CT were identified in multivariable analysis. Four algorithms based on different combinations of behavioural factors, clinical findings and vaginal microscopy were developed. Performance of each algorithm was evaluated for detecting vaginal and cervical infections separately. Cost effectiveness was based on cost per patient treated and cost per case correctly treated. Sensitivity analysis explored the influence of NG/CT prevalence on cost effectiveness. RESULTS: 60% (252/420) of women had genital infections, with 9.5% (40/423) having NG/CT. Factors associated with NG/CT included new and multiple sexual partners in the past 3 months, symptomatic partner, childlessness and >or=10 polymorphonuclear cells per field on vaginal microscopy. For NG/CT detection, the algorithm that relied solely on behavioural risk factors was less sensitive but more specific than those that included speculum examination or microscopy but had higher correct-treatment rate and lower over-treatment rates. The cost per true case treated using a combination of risk factors, speculum examination and microscopy was euro 24.08. A halving and tripling of NG/CT prevalence would have approximately the inverse impact on the cost-effectiveness estimates. CONCLUSIONS: Management of NG/CT in Bulgaria was improved by the use of a syndromic approach that included risk scores. Approaches that did not rely on microscopy lost sensitivity but were more cost effective

Using Synchronic and Diachronic Relations for Summarizing Multiple Documents Describing Evolving Events

In this paper we present a fresh look at the problem of summarizing evolving events from multiple sources. After a discussion concerning the nature of evolving events we introduce a distinction between linearly and non-linearly evolving events. We present then a general methodology for the automatic creation of summaries from evolving events. At its heart lie the notions of Synchronic and Diachronic cross-document Relations (SDRs), whose aim is the identification of similarities and differences between sources, from a synchronical and diachronical perspective. SDRs do not connect documents or textual elements found therein, but structures one might call messages. Applying this methodology will yield a set of messages and relations, SDRs, connecting them, that is a graph which we call grid. We will show how such a grid can be considered as the starting point of a Natural Language Generation System. The methodology is evaluated in two case-studies, one for linearly evolving events (descriptions of football matches) and another one for non-linearly evolving events (terrorist incidents involving hostages). In both cases we evaluate the results produced by our computational systems.Comment: 45 pages, 6 figures. To appear in the Journal of Intelligent Information System

Abstraction in directed model checking

Abstraction is one of the most important issues to cope with large and infinite state spaces in model checking and to reduce the verification efforts. The abstract system is smaller than the original one and if the abstract system satisfies a correctness specification, so does the concrete one. However, abstractions may introduce a behavior violating the specification that is not present in the original system. This paper bypasses this problem by proposing the combination of abstraction with heuristic search to improve error detection. The abstract system is explored in order to create a database that stores the exact distances from abstract states to the set of abstract error states. To check, whether or not the abstract behavior is present in the original system, effcient exploration algorithms exploit the database as a guidance