9 research outputs found
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/
Specific, simple and rapid detection of porcine circovirus type 2 using the loop-mediated isothermal amplification method
<p>Abstract</p> <p>Background</p> <p>Porcine circovirus type 2 (PCV2) is the causative agent of postweaning multisystemic wasting syndrome (PMWS), and porcine dermatitis and nephropathy syndrome (PDNS). It has caused heavy losses in global agriculture in recent decades. Rapid detection of PCV2 is very important for the effective prophylaxis and treatment of PMWS.</p> <p>Results</p> <p>A loop-mediated isothermal amplification (LAMP) assay was used to detect PCV2 in this study. Three pairs of primers were specially designed for recognizing eight distinct sequences of the ORF2 gene. This gene lies in the PCV2 virus genome sequence, and encodes the Rep protein that is involved in virus replication. Time and temperature conditions for amplification of PCV2 genes were optimized to be 55 min at 59°C. The analysis of clinical samples indicated that the LAMP method was highly sensitive. The detection limit for PCV2 by the LAMP assay was 10 copies, whereas the limit by conventional PCR was 1000 copies. The assay did not cross-react with PCV1, porcine reproductive and respiratory syndrome virus, porcine epidemic diarrhea virus, transmissible gastroenteritis of pigs virus or rotavirus. When 110 samples were tested using the established LAMP system, 95 were detected as positive.</p> <p>Conclusion</p> <p>The newly developed LAMP detection method for PCV2 was more specific, sensitive, rapid and simple than before. It complements and extends previous methods for PCV2 detection and provides an alternative approach for detection of PCV2.</p
A novel information search approach for languages without word delimiters
Summary In many languages there are no word delimiters among the text. It is very difficult to index articles in those languages. For example, Chinese information search engines always encounter a difficulty in segmentation of Chinese words from an article. In this paper, a suffix tree based searching approach is proposed to avoid the difficulty in segmentation of Chinese words. The suffix tree algorithms are studied and a set of optimal algorithms for index build are proposed. Based on the algorithms, a prototype of Chinese information search system is developed and applied to the Chinese Web Test collection with 100 GB Web pages (CWT100g). The experimental results show that the system is capable of searching Chinese information without segmentation of Chinese words and the speed of index build is reduced to the theoretical limitation. part of summary
Evolutionary Machine Learning for RTS Game StarCraft
Real-Time Strategy (RTS) games involve multiple agents acting simultaneously, and result in enormous state dimensionality. In this paper, we propose an abstracted and simplified model for the famous game StarCraft, and design a dynamic programming algorithm to solve the building order problem, which takes minimal time to achieve a specific target. In addition, Genetic Algorithms (GA) are used to find an optimal target for the opening stage