Reasoning about Rational, but not Logically Omniscient Agents

We propose in the paper a new solution to the so-called Logical Omniscience Problem of epistemic logic. Almost all attempts in the literature to solve this problem consist in weakening the standard epistemic systems: weaker systems are considered where the agents do not possess the full reasoning capacities of ideal reasoners. We shall argue that this solution is not satisfactory: in this way omniscience can be avoided, but many intuitions about the concepts of knowledge and belief get lost. We shall show that axioms for epistemic logics must have the following form: if the agent knows all premises of a valid inference rule, and if she thinks hard enough, then she will know the conclusion. To formalize such an idea, we propose to \dynamize' epistemic logic, that is, to introduce a dynamic component into the language. We develop a logic based on this idea and show that it is suitable for formalizing the notion of actual, or explicit knowledge

On the epistemic foundations of agent theories

We argue that none of the existing epistemic logics can adequately serve the needs of agent theories. We suggest a new concept of knowledge which generalizes both implicit and explicit knowledge and argue that this is the notion we need to formalize agents in Distributed Artificial Intelligence. A logic of the new concept is developed which is formally and practically adequate in the following sense: first, it does not suffer from any kind of logical omniscience. Second, it can account for the intuition that agents are rational, though not hyper-rational. Third, it is expressive enough. The advantages of the new logic over other formalisms is demonstrated by showing that none of the existing systems can fulfill all these requirements simultaneously

Resource-Bounded Reasoning about Knowledge

Der Begriff ``Agent'''' hat sich als eine sehr nützliche Abstraktion erwiesen, um verschiedene Problembereiche auf eine intuitive und natürliche Art und Weise zu konzeptualisieren. Intelligente Agenten haben daher Anwendung gefunden in verschiedenen Teilbereichen der Informatik. Zur Modellierung werden intelligente Agenten meist als intentionale Systeme aufgefaßt und mit Hilfe von mentalistischen Begriffen wie Wissen, Glauben (oder Überzeugung), Wunsch, Pflicht, Intention usw. beschrieben. Unter diesen mentalen Begriffen gehören die epistemischen Begriffe (d.h., Wissen und Glauben) zu den wichtigsten und wurden auch am intensivsten untersucht. Zur Modellierung von Wissen und Glauben werden in der Regel modale epistemische Logiken verwendet. Solche Systeme sind aber nicht geeignet, um ressourcenbeschränkte Agenten zu beschreiben, weil sie zu starke Annahmen bezüglich der Rationalität von Agenten machen. Zum Beispiel wird angenommen, daß Agenten alle logischen Wahrheiten sowie alle Konsequenzen seines Wissens kennen. Dieses Problem ist bekannt als das Problem der logischen Allwissenheit (``logical omniscience problem''''). Da alle Agenten grundsätzlich nur über begrenzte Ressourcen (wie z.B. Zeit, Information, Speicherplatz) verfügen, können sie nur eine begrenzte Menge von Informationen verarbeiten. Daher müssen alternative Modelle entwickelt werden, um Agenten realistisch modellieren zu können (siehe Kapitel 2). Daß modale epistemische Logik für die Formalisierung des ressourcenbeschränkten Schließens (``resource-bounded reasoning'''') nicht geeignet ist, wird als ein offenes Problem der Agententheorien anerkannt. Es gibt bisher aber keine brauchbaren Alternativen zur Modallogik. Die meisten Ansätze zur Lösung des logischen Allwissenheitsproblems versuchen, Wissen und Glauben mit Hilfe schwacher Modallogiken zu beschreiben. Solche Versuche sind nicht befriedigend, da sie eine willkürliche Einschränkung der Rationalität der Agenten zur Folge haben (siehe Kapitel 3). Mein Ziel ist es, einen Rahmen für das ressourcenbeschränktes Schließen über Wissen und Glauben zu entwickeln. Damit soll eine solide Grundlage für Theorien intelligenter Agenten geschaffen werden. Als Nebenergebnis wird das logische Allwissenheitsproblem auf eine sehr intuitive Art und Weise gelöst: obwohl Agenten rational sind und alle logischen Schlußregeln anwenden können, sind sie nicht logisch allwissend, weil ihnen nicht genügend Ressourcen zu Verfügung stehen, um alle logischen Konsequenzen ihres Wissens zu ziehen. Im Kapitel 4 wird eine Reihe von Logiken vorgestellt, die den Begriff des expliziten Wissens formalisieren. Es wird eine Lösung des Problems der logischen Allwissenheit der epistemischen Logik vorgeschlagen, die die Rationalität der Agenten nicht willkürlich einschränkt. Der Grundgedanke dabei ist der folgende. Ein Agent kennt die logischen Konsequenzen seines Wissens nur dann, wenn er sie tatsächlich hergeleitet hat. Wenn ein Agent alle Prämissen einer gültigen Schlußregel kennt, kennt er nicht notwendigerweise die Konklusion: er kennt sie nur nach der Anwendung der Regel. Wenn er den Schluß nicht ziehen kann, z.B. weil er nicht die notwendigen Ressourcen dazu hat, wird sein Wissen nicht um diese herleitbare Information erweitert. Die Herleitung neuer Informationen wird als die Ausführung mentaler Handlungen aufgefaßt. Mit Hilfe einer Variante der dynamischen Logik können diese Handlungen beschrieben werden. Im Kapitel 5 werden Systeme für das ressourcenbeschränkte Schließen über Wissen und Glauben entwickelt, die auch quantitative Bedingungen über die Verfügbarkeit von Ressourcen modellieren können. Mit Hilfe dieser Logiken können Situationen beschrieben werden, wo Agenten innerhalb einer bestimmten Zeitspanne entscheiden müssen, welche Handlungen sie ausführen sollen. Der Ansatz besteht darin, epistemische Logik mit Komplexitätstheorie zu verbinden. Mit Hilfe einer Komplexitätsanalyse kann ein Agent feststellen, ob ein bestimmtes Problem innerhalb vorgegebener Zeit lösbar ist. Auf der Grundlage dieses Wissens kann er dann die für die Situation geeignete Entscheidung treffen. Damit ist es gelungen, eine direkte Verbindung zwischen dem Wissen eines Agenten und der Verfügbarkeit seiner Ressourcen herzustellen.One of the principal goals of agent theories is to describe realistic, implementable agents, that is, those which have actually been constructed or are at least in principle implementable. That goal cannot be reached if the inherent resource-boundedness of agents is not treated correctly. Since the modal approach to epistemic logic is not suited to formalize resource-bounded reasoning, the issue of resource-boundedness remains one of the main foundational problems of any agent theory that is developed on the basis of modal epistemic logic. My work is an attempt to provide theories of agency with a more adequate epistemic foundation. It aims at developing theories of mental concepts that make much more realistic assumptions about agents than other theories. The guiding principle of my theory is that the capacities attributed to agents must be empirically verifiable, that is, it must be possible to construct artificial agents which satisfy the specifications determined by the theory. As a consequence, the unrealistic assumption that agents have unlimited reasoning capacities must be rejected. To achieve the goal of describing resource-bounded agents accurately, the cost of reasoning must be taken seriously. In the thesis I have developed a framework for modeling the relationship between knowledge, reasoning, and the availability of resources. I have argued that the correct form of an axiom for epistemic logic should be: if an agent knows all premises of a valid inference rule and if he performs the right reasoning, then he will know the conclusion as well. Because reasoning requires resources, it cannot be safely assumed that the agent can compute his knowledge if he does not have enough resources to perform the required reasoning. I have demonstrated that on the basis of that idea, the problems of traditional approaches can be avoided and rich epistemic logics can be developed which can account adequately for our intuitions about knowledge

Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning

Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field learning head to effectively handle the irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin.Comment: Accepted at The 12th International Conference on Awareness Science and Technolog

TÍNH CHẤT PHI CỔ ĐIỂN CỦA TRẠNG THÁI KẾT HỢP CẶP THÊM VÀ BỚT PHOTON HAI MODE

In this paper, we study the non-classical properties such as two-mode sum squeezing, two-mode difference squeezing, and higher-order two-mode antibunching properties of the photon-added-and-subtracted two-mode pair coherent state (PAASTMPCS). The results in the squeezing property show that the PAASTMPCS has two-mode sum squeezing but no two-mode difference squeezing. The two-mode sum squeezing of the PAASTMPCS always appears when adding and subtracting photons to two modes of a pair coherent state (PCS). Furthermore, the PAASTMPCS has higher-order two-mode antibunching in any order, and this property is enhanced when photons are simultaneously added and subtracted to two modes of the PCS. Thereby, the role of the photon addition and the photon subtraction has been confirmed by enhancing the non-classical properties of the PAASTMPCS.Trong bài báo này chúng tôi nghiên cứu các tính chất phi cổ điển như tính chất nén tổng hai mode, nén hiệu hai mode và tính chất phản kết chùm hai mode bậc cao của trạng thái kết hợp cặp thêm và bớt photon hai mode (PAASTMPCS). Các kết quả khảo sát về tính chất nén cho thấy rằng trạng thái PAASTMPCS có tính chất nén tổng hai mode nhưng không có tính chất nén hiệu hai mode. Tính chất nén tổng hai mode của trạng thái PAASTMPCS luôn xuất hiện khi thêm và bớt photon vào trạng thái kết hợp cặp (PCS). Ngoài ra, kết quả khảo sát chỉ ra rằng trạng thái PAASTMPCS còn có tính chất phản kết chùm hai mode bậc cao và tính chất này được tăng cường khi thêm và bớt photon vào PCS. Qua đó, vai trò của việc thêm và bớt photon đã được khẳng định thông qua việc tăng cường tính chất phi cổ điển của trạng thái PAASTMPCS

Detection of Japanese encephalitis virus and its specific antibody in abnormal swine litters in Vietnam

Japanese encephalitis is considered an essential disease-causing swine reproductive failure in Asian countries; however, significant knowledge gaps remain about this problem in actual cases. This study examined 55 cases of reproductive failure in sows, including one abortion and 54 full-term farrowing litters, to find the Japanese encephalitis virus (JEV) and specific antibodies against JEV. Haemagglutination test was used to detect specific antibodies against JEV from 63 samples of fetal thoracic fluids and newborn un-suckling piglet sera (54 thoracic fluid samples of dead fetuses, two thoracic fluid samples of abortion fetuses, seven sera samples of weakly newborn un-suckling piglets), viral isolation and RT- PCR technique was attempted from 60 brains of fetuses and newborn un-suckling piglets, histopathology sections of fetal brains which were positive with JEV confirmed by HI test and RT-PCR were also made. The results showed that 17.46% (11/63) of thoracic fluids and sera were positive for JEV. No JEV isolation was found from 60 brain samples of dead fetuses and piglets, but RNA of JEV were detected from 5 of them (8.33%). Our results suggest that JEV should be considered the important cause of swine reproductive failure in Viet Nam

INFLUENCE OF SYNTHESIS FACTORS ON PROPERTIES OF GEOPOLYMERS BASED ON RED MUD AND RICE HUSK ASH

In this paper, the effect of mixing ratio of raw materials, curing temperature and time on geopolymerization between red mud and rice husk ash were investigated. The results showed that the optimum conditions were SiO2/Al2O3 ratio of 4 and Na2O/Al2O3 ratio of 2.0, curing temperature of 100oC and curing time of 24 hours. The compressive strength, bulk density, total shrinkage of the obtained product were 22.8 MPa, 2.39 g.cm-3, 15%, respectively that met requirement of unsintered bricks using for construction

Nonparametric Regression-based Step-length Estimation for Arm-swing Walking using a Smartphone

In this paper, we propose an adaptive step-estimation method to estimate the distance traveled for arm-swinging activities at three level-walking speeds, i.e., low, normal, and high speed. The proposed method is constructed based on a polynomial function of the pedestrian speed and variance of walking acceleration. We firstly apply a low-pass filter with 10 Hz cut-off frequency for acceleration data. Then, we analyze the acceleration data to find the number of steps in each sample. Finally, the traveled distance is calculated by summing all step lengths which are estimated by the proposed method during walking. Applying the proposed method, we can estimate the walking distance with an accuracy rate of 95.35% in a normal walking speed. The accuracy rates of low and high walking speeds are 94.63% and 94.97%, respectively. Furthermore, the proposed method outperforms conventional methods in terms of accuracy and standard deviation at low, normal, and high speeds