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"Today's expert systems deal with domains of narrow specialization. For expert systems to perform competently over a broad range of tasks, they will have to be given very much more knowledge. ... The next generation of expert systems ... will require large knowledge bases. How will we get them?"
– Edward Feigenbaum, Pamela McCorduck, H. Penny Nii, from The Rise of the Expert Company. New York: Times Books, 1988.
In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.
Knowledge base question answering (KBQA) aims to answer a natural language question over a knowledge base (KB) as its knowledge source. A knowledge base (KB) is a structured database that contains a collection of facts in the form subject, relation, object, where each fact can have properties attached called qualifiers. For example, the sentence "Barack Obama got married to Michelle Obama on 3 October 1992 at Trinity United Church" can be represented by the tuple Barack Obama, Spouse, Michelle Obama, with the qualifiers start time 3 October 1992 and place of marriage Trinity United Church . Popular knowledge bases are DBpedia and WikiData. Early works on KBQA focused on simple question answering, where there's only a single fact involved.
For example, consider the case where the perception module detects a pedestrian (PCV) on the road. It does not, however, recognize that the pedestrian is jaywalking. Even if no jaywalking events have been seen while training the CV perception module, representing knowledge of this event – i.e. (Pedestrian, participatesIn, Jaywalking) – in the scene KG could provide a new insight or cue for handling this edge-case with KEP (i.e.
TikTok users will soon have even more ways to make their videos stand out from the crowd. The service has announced the TikTok Library, which will grant creators access to more entertainment-based content. You'll be able to find GIFs, clips from your favorite TV shows, memes and other content, which you can slot into your TikToks. Although there are already ways to insert GIFs from Giphy into TikTok videos, it should be easier to do that once you have access to the library. Until now, Giphy GIFs have been available as Stickers and via the Green Screen effect.
IL algorithms can be grouped broadly into (a) online, (b) offline, and (c) interactive methods. We provide, for each setting, performance bounds for learned policies that apply for all algorithms, provably efficient algorithmic templates for achieving said bounds, and practical realizations that out-perform recent work. From beating the world champion at Go (Silver et al.) to getting cars to drive themselves (Bojarski et al.), we've seen unprecedented successes in learning to make sequential decisions over the last few years. When viewed from an algorithmic viewpoint, many of these accomplishments share a common paradigm: imitation learning (IL). In imitation learning, one is given access to samples of expert behavior (e.g.
The trend for an aging population, which is typical for Europe and for other high-income regions, brings with it a sharp increase in the number of chronic patients and a shortage of clinicians and hospital beds. Evidence-based clinical decision-support systems are one of the promising solutions for this problem.15 In the 1990s, different research groups started to develop computer-interpretable clinical guidelines (CIGs)7 as a form of evidence-based decision-support systems (DSS). Narrative evidence-based clinical guidelines, focused on a single disease, and containing recommendations for the disease diagnosis and management, were manually represented in CIG formalisms, such as Asbru,11 GLIF,1 or PROforma.3 The CIGs formed a network of clinical decisions and actions and served as a knowledge base.
Japan will strengthen its consultation system for fertility treatment as its public health insurance program starts covering such treatment in April. The health ministry plans to integrate related public consultation windows under a single system. The new facilities will help people with specialist advice and provide emotional support to women who feel anxious. In the fiscal 2022 revision of official medical fees, the public insurance coverage will be extended to fertility treatment such as in vitro fertilization and artificial insemination as part of efforts to shore up the country's falling birthrate. Thanks to this, costs of fertility treatment that have been fully paid by patients will be limited to 30% in principle.
Artificial intelligence has pioneered new technologies in the education for classroom engagements and in school systems on a broader dimension with huge potential to promote education. Haugeland defines AI as the exciting new effort to make computers think… machines with minds, in the full and literal sense. This article focuses on engineering education in a knowledge society with effectiveness in view. It examines the technologies in current use, applications, and future possibilities. It concludes that effectiveness is a continuously improvable process as we iterate towards a desirable future. Today's education model largely focuses on one instructor providing information to several learners at the same time.
IN the last part we discussed what is machine learning, the history of machine learning, how the data is used and the use cases of machine learning. Now in this part we are going to discuss the difference between AI vs ML vs DL. Most of the beginners when try to get into this field, they are curious to know about what actually is the difference between AI, ML and DL. When we google this term we got to see this picture. The outermost section represents AI, the middle section represents ML and the innermost represents DL.