Goal Recognition Design (GRD) is the problem of designing a domain in a way that will allow easy identification of agents' goals. This work extends the original GRD problem to the Plan Recognition Design (PRD) problem which is the task of designing a domain using plan libraries in order to facilitate fast identification of an agent's plan. While GRD can help to explain faster which goal the agent is trying to achieve, PRD can help in faster understanding of how the agent is going to achieve its goal. we define a new measure that quantifies the worst-case distinctiveness of a given planning domain, propose a method to reduce it in a given domain and show the reduction of this new measure in three domains from the literature.

Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in an environment. In this work, we address the problem of activity recognition in an indoor environment, focusing on a kitchen scenario. Unlike existing approaches that identify single actions from video sequences, we also identify the goal towards which the subject of the video is pursuing. Our hybrid approach combines a deep learning architecture to analyze raw video data and identify individual actions which are then processed by a goal recognition algorithm that uses a plan library describing possible overarching activities to identify the ultimate goal of the subject in the video. Experiments show that our approach achieves the state-of-the-art for identifying cooking activities in a kitchen scenario.

It may (2010) to estimate the probability of each possible goal be that one agent needs to monitor the activities of another based on the difference between the cost of the best plan agent, attempt to assist the other agent, or simply avoid getting for the goal given the observed actions, Cost(G O), and the in the way while performing its own duties. For all of cost of the best plan for the goal without the observed actions, these cases the agent needs to be able to realize what the Cost(G O). The big difference here is that the observations other agent is doing. In the absence of full and timely communication only indirectly give us probabilities for actions in of plans and goals, goal and plan recognition becomes the plan graph. We therefore first construct a Bayesian Network essential. Many goal recognition techniques allow the (BN) to estimate these action probabilities, and then sequence of observations to be incomplete, but few consider use this probability information in the plan graph to compute the possibility of noisy observations. In practice, this is not expected cost for each goal, given the observations.

Knowledge Graph (KG) plays a crucial role in many modern applications. Nevertheless, constructing KG from unstructured text is a challenging problem due to its nature. Consequently, many approaches propose to transform unstructured text to structured text in order to create a KG. Such approaches cannot yet provide reasonable results for mapping an extracted predicate to its identical predicate in another KG. Predicate mapping is an essential procedure because it can reduce the heterogeneity problem and increase searchability over a KG. In this paper, we propose T2KG system, an end-to-end system with keeping such problem into consideration. In the system, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its identical predicate in a KG. Based on preliminary experimental results, the hybrid approach improves the recall by 10.02% and the F-measure by 6.56% without reducing the precision in the predicate mapping task. Furthermore, although the KG creation is conducted in open domains, the system still achieves approximately 50% of F-measure for generating triples in the KG creation task.

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge-graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significantly outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks.

To ensure we do not forget relevant aspects of AI, we The field of Artificial Intelligence (AI) is fertile: it is at the present some key works which have already focused on same time the root of the dreams and deceptions of many defining (artificial) intelligence in Section 2. We then highlight people, a common feature in science fiction, and various the potential lack of cross-fertilisation they may be subject technical projects in many domains of application. Although to in Section 3 and consider the definition of human we may appreciate the rich emotions and ideas brought by expertise to draw a definition of human intelligence in Section a concept such as AI, some people are seriously working on 4. Next, we generalise these definitions to cover also artificial it in an attempt to produce autonomous agents able to meet agents in Section 5 and provide more details about the the various needs of different users. These projects, however, domain-generic data and processes of our definition of intelligence have faced several troubles and unfulfilled promises in in Section 6. We rely further on the expertise field in the history of the field, leading to shortenings of funding Section 7 by describing three kinds of measures of expertise, and years of research efforts lost (Franklin 2014). Despite mapping them to existing measures of intelligence, and suggesting the presence of "intrepid researchers" to advance the field, directions to investigate. Finally, Section 8 expands from an industrial point of view such projects were abandoned the discussion to a novel conception of the field of AI as a and considered as failures.

The problem of ordering documents in a large collection into a sequence that is efficient for learning (both human and machine) is of high practical significance, but has not yet been well-formulated. We formulate this problem as mining a maximally informative simple sequence of documents. The mined sequence should be maximally informative in the sense that the reader learns quickly by reading only a few documents, and it should be simple so that the reader is not overwhelmed while trying to learn the content. The task can be posed as: Given that a reader wishes to read (at most) k documents, which documents should be selected from the repository and in what order, so as to provide maximum information. We present the WikiSeq algorithm for this purpose. We also design a metric based on information-gain to help objectively evaluate WikiSeq, and conduct experiments to compare with indicative baselines. Finally, we provide case-studies to subjectively illustrate WikiSeq’s merits.

However, instead of treating the skin lesion classification Visual aspects of skin diseases, especially skin lesions, play as a standalone problem and training a CNN model a key role in dermatological diagnosis. A successful identification using skin lesion labels only, we further propose to jointly of the skin lesion allows skin disorders to be placed in optimize the skin lesion classification with a related auxiliary certain diagnostic categories where specific diagnosis can be task, body location classification. The motivation behind established (Cecil, Goldman, and Schafer 2012). However, this design is to make use of the body site predilection categorization of skin lesions is a challenging process. It of skin diseases (Cox and Coulson 2004) as it has long usually involves identifying the specific morphology, distribution, been recognized by dermatologists that many skin diseases color, shape and arrangement of lesions. When these and their corresponding skin lesions are correlated with their components are analyzed separately, the differentiation of body site manifestation. For example, a skin lesion caused skin lesions can be quite complex and requires a great deal by sun exposure is only present in sun-exposed areas of the of experience and expertise (Lawrence and Cox 2002).

We envision an integrated framework for supporting the development and deployment of human-aware, general artificial intelligence (AI) that needs to collaborate in uncertain, changing environments. We examine the technology and system requirements of building assistive care agents for dementia or cognitive impaired patients through the continuum of care. We summarize the new AI capabilities and show examples of how an evolving, adaptive development approach would be able to support the basic functionalities and applications in a sound, practical, and scalable manner. We highlight the challenges and the opportunities involved in realizing the proposed framework, and call for future research and development efforts from the AI community to work in this challenging and important domain.

Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population’s vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.