Programming deliberation strategies in meta-APL

A key advantage of BDI-based agent programming is that agents can deliberate about which course of action to adopt to achieve a goal or respond to an event. However, while state-of-the-art BDI-based agent programming languages provide flexible support for expressing plans, they are typically limited to a single, hard-coded, deliberation strategy (perhaps with some parameterisation) for all task environments. In this paper, we present an alternative approach. We show how both agent programs and the agent’s deliberation strategy can be encoded in the agent programming language meta-APL. Key steps in the execution cycle of meta-APL are reflected in the state of the agent and can be queried and updated by meta-APL rules, allowing BDI deliberation strategies to be programmed with ease. To illustrate the flexibility of meta-APL, we show how three typical BDI deliberation strategies can be programmed using meta-APL rules. We then show how meta-APL can used to program a novel adaptive deliberation strategy that avoids interference between intentions

Representing Aboutness: Automatically Indexing 19th- Century Encyclopedia Britannica Entries

Representing aboutness is a challenge for humanities documents, given the linguistic indeterminacy of the text. The challenge is even greater when applying automatic indexing to historical documents for a multidisciplinary collection, such as encyclopedias. The research presented in this paper explores this challenge with an automatic indexing comparative study examining topic relevance. The setting is the NEH-funded 19th-Century Knowledge Project, where researchers in the Digital Scholarship Center, Temple University, and the Metadata Research Center, Drexel University, are investigating the best way to index entries across four historical editions of the Encyclopedia Britannica (3rd, 7th, 9th, and 11th editions). Individual encyclopedia entry entries were processed using the Helping Interdisciplinary Vocabulary Engineering (HIVE) system, a linked-data, automatic indexing terminology application that uses controlled vocabularies. Comparative topic relevance evaluation was performed for three separate keyword extraction algorithms: RAKE, Maui, and Kea++. Results show that RAKE performed the best, with an average of 67% precision for RAKE, and 28% precision for both Maui and Kea++. Additionally, the highest-ranked HIVE results with both RAKE and Kea++ demonstrated relevance across all sample entries, while Maui’s highest-ranked results returned zero relevant terms. This paper reports on background information, research objectives and methods, results, and future research prospects for further optimization of RAKE’s algorithm parameters to accommodate for encyclopedia entries of different lengths, and evaluating the indexing impact of correcting the historical Long S

Operational modal characterisation for long-term monitoring in offshore structures

In the effort to increase sustainability and help drive the energy sector to support oil and gas decarbonisation, the importance of identifying and characterising the current state of assets is becoming ever more crucial for operators, especially with regards to life extension and repurposing. Structural Health Monitoring (SHM) offers a promising and forward-thinking methodology for tracking asset integrity. In particular, Operational Modal Analysis (OMA) presents a proven, output only method for structure characterisation and modal tracking. However, Environmental and Operational Variations (EOV) can influence the modal properties of the structure making modal parameter estimation and tracking less reliable in the short- and long-term. This study employs an Automated Operational Modal Analysis (AOMA) methodology based on the combination of stochastic subspace identification and natural frequency histogram bin analysis for robust parameter extraction. The methodology is implemented in a complex of three bridge linked offshore platforms. Structural complexity, operational loading and significant modal couplings require sophisticated analysis. The preliminary results show discontinuity of the operational modes over a 6-month long monitoring period. The findings demonstrate the need of further analysis to understand the time-variant parameters which determine structural response across the asset lifetime

Programming agent deliberation using procedural reflection

A key advantage of BDI-based approaches to agent programming, is that agents can deliberate about which course of action to adopt to achieve a goal or respond to an event. However, while state-of-the-art BDI-based agent programming languages allow the programmer to specify the context(s) in which a particular plan is applicable, they are typically limited to a single, hard-coded, deliberation strategy for all task environments. In this paper, we present an alternative approach, in which an agent’s deliberation strategy forms part of the agent program. We show how both conventional agent programs and the agent’s deliberation strategy can be encoded in the agent programming language meta-APL. Key steps in the deliberation cycle of meta-APL are reflected in the state of the agent and can be queried and updated by meta-APL rules, allowing application-specific BDI deliberation strategies to be programmed in a straightforward way. To illustrate the flexibility of meta-APL, we show how three typical BDI deliberation strategies can be programmed using meta-APL rules. We then show how meta-APL can used to program a simple adaptive deliberation strategy that avoids interference between intentions

Heffner Wetland Facility Roof Garden

Course Code: ENR 2367A green roof at the Heffner building, located at the Wilma H. Scheiermeier Olentangy River Wetland Park, would benefit the University and the surrounding community socially, economically, and ecologically. Using student research, this project outlines a plan for the construction of a roof garden on top of the Heffner facility.Academic Major: Environment, Economy, Development, and SustainabilityAcademic Major: Environmental ScienceAcademic Major: FrenchAcademic Major: MathematicsAcademic Major: Sustainable Plant System

Isotope Depletion Mass Spectrometry (ID-MS) for Accurate Mass Determination and Improved Top-Down Sequence Coverage of Intact Proteins

Top-down mass spectrometry (MS) is an increasingly important technique for protein characterization. However, in many biological MS experiments, the practicality of applying top-down methodologies is still limited at higher molecular mass. In large part, this is due to the detrimental effect resulting from the partitioning of the mass spectral signal into an increasing number of isotopic peaks as molecular mass increases. Reducing the isotopologue distribution of proteins via depletion of heavy stable isotopes was first reported over 20 years ago (Marshall, A. G.; Senko, M. W.; Li, W.; Li, M.; Dillon, S., Guan, S.; Logan, T. M.. Protein Molecular Mass to 1 Da by 13C, 15N Double-Depletion and FT-ICR Mass Spectrometry. J. Am. Chem. Soc. 1997, 119, 433−434.) and has been demonstrated for several small proteins. Here we extend this approach, introducing a new highly efficient method for the production of recombinant proteins depleted in 13C and 15N and demonstrating its advantages for top-down analysis of larger proteins (up to ∼50 kDa). FT-ICR MS of isotopically depleted proteins reveals dramatically reduced isotope distributions with monoisotopic signal observed up to 50 kDa. In top-down fragmentation experiments, the reduced spectral complexity alleviates fragment-ion signal overlap, the presence of monoisotopic signals allows assignment with higher mass accuracy, and the dramatic increase in signal-to-noise ratio (up to 7-fold) permits vastly reduced acquisition times. These compounding benefits allow the assignment of ∼3-fold more fragment ions than comparable analyses of proteins with natural isotopic abundances. Finally, we demonstrate greatly increased sequence coverage in time-limited top-down experiments—highlighting advantages for top-down LC–MS/MS workflows and top-down proteomics

A Strategic Vision for Telemedicine and Medical Informatics in Space Flight

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63255/1/15305620050503924.pd

Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games

Video games have served as useful benchmarks for the decision making community, but going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community. Recent progress in the research, development and open release of large vision models has the potential to amortize some of these costs across the community. However, it is currently unclear which of these models have learnt representations that retain information critical for sequential decision making. Towards enabling wider participation in the research of gameplaying agents in modern games, we present a systematic study of imitation learning with publicly available visual encoders compared to the typical, task-specific, end-to-end training approach in Minecraft, Minecraft Dungeons and Counter-Strike: Global Offensive.Comment: Preprin