Handling temporality in human activity reasoning

Human-aware Artificial Intelligent systems are goal directed autonomous systems that are capable of interacting, collaborating, and teaming with humans. Activity reasoning is a formal reasoning approach that aims to provide common sense reasoning capabilities to these interactive and intelligent systems. This reasoning can be done by considering evidences –which may be conflicting–related to activities a human performs. In this context, it is important to consider the temporality of such evidence in order to distinguish activities and to analyse the relations between activities. Our approach is based on formal argumentation reasoning, specifically, Timed Argumentation Frameworks (TAF), which is an appropriate technique for dealing with inconsistencies in knowledge bases. Our approach involves two steps: local selection and global selection. In the local selection, a model of the world and of the human’s mind is constructed in form of hypothetical fragments of activities (pieces of evidences) by considering a set of observations. These hypothetical fragments have two kinds of relations: a conflict relation and a temporal relation. Based on these relations, the argumentation attack notion is defined. We define two forms of attacks namely the strong and the weak attack. The former has the same characteristics of attacks in TAF whereas for the latter the TAF approach has to be extended. For determining consistent sets of hypothetical fragments, that are part of an activity or are part of a set of non-conflicting activities, extension-based argumentation semantics are applied. In the global selection, the degrees of fulfillment of activities is determined. We study some properties of our approach and apply it to a scenario where a human performs activities with different temporal relations.

Sentiment Extraction: Integrating Statistical Parsing, Semantic Analysis, and Common Sense Reasoning

Much of the ongoing explosion of digital content is in the form of text. This content is a virtual gold-mine of information that can inform a range of social, governmental, and business decisions. For example, using content available on blogs and social networking sites businesses can find out what its customers are saying about their products and services. In the digital age where customer is king, the business value of ascertaining consumer sentiment cannot be overstated. People express sentiments in myriad ways. At times, they use simple, direct assertions, but most often they use sentences involving comparisons, conjunctions expressing multiple and possibly opposing sentiments about multiple features and entities,and pronominal references whose resolution requires discourse level context. Frequently people use abbreviations, slang, SMSese, idioms and metaphors. Understanding the latter also requires common sense reasoning. In this paper, we present iSEE, a fully implemented sentiment extraction engine, which makes use of statistical methods, classical NLU techniques, common sense reasoning, and probabilistic inference to extract entity and feature specific sentiment from complex sentences and dialog. Most of the components of iSEE are domain independent and the system can be generalized to new domains by simply adding domain relevant lexicons.

Reasoning and Argumentation

The psychology of reasoning and argumentation studies how people reason and persuade others using language. Influenced by analytic philosophy, much early work focused on the degree to which verbal reasoning is captured by or diverges from classical deductive logic. From this viewpoint, human thinking can seem prone to substantial and systematic bias. Since 1994, verbal reasoning has been set in the context of uncertain, common-sense reasoning rather than deduction, and reasoning has been seen as continuous with the social challenge of real-world argumentation. From this perspective, the human ability to reason and argue with words is better considered not as flawed logical reasoning, but as often highly competent reasoning and persuasion in an uncertain and contested world.

MU‘ĀRAḌAH SEBAGAI METODE MEMAHAMI ‘ILLAH PADA MATAN HADIS

One of the conditions for the validity of a hadith is that it is protected from 'illah. However, what is meant by 'illah in the hadiths and how to know if a hadith contains' illah or not, does not seem to have received much attention from hadith scholars, so the issue of' illah is still being debated among scholars and hadith researchers. This study intends to explain this problem. The research method is literature study using descriptive analysis. From the research conducted, it is known that 'illah does not get as much attention from the scholars of hadith, as it does for the validity of other hadiths. It is also known that the scholars use the word ‘illah’ in the sense of the language, namely something that enters something else, then causes what it enters to change. 'Illah is usually interpreted as a disease, because if the disease enters the body, the body will become weak, the same as' illah if it is entered into a hadith, it will change the quality of the hadith to become weak. The way to find out the existence of 'illah in this hadith is by doing mu'āraḍah, which is the matching of the concepts that are the main content of every hadith object so that the interconnection and harmony between the concepts and other shari'a arguments are maintained, namely with the explicit instructions of the Koran, other traditions, historical knowledge and common sense reasoning. Abstrak salah satu syarat kesahihan suatu hadis adalah bahwa hadis itu terhindar dari ‘illah. Namun apakah yang dimaksud dengan ‘illah pada hadis dan bagiamana cara mengetahui suatu hadis mengandung ‘illah atau tidak, tampaknya belum mendapat perhatian yang besar dari ulama hadis, sehingga masalah ‘illah ini masih terus diperdebatkan di kalangan ulama dan peneliti hadis. Penelitian ini bermaksud menjelaskan masalah tersebut. Metode penelitian bersifat studi kepustakaan dengan menggunakan analisis deskriptif. Dari penelitian yang dilakukan diketahui bahwa ‘illah tidak mendapatkan perhatian yang besar dari para ulama hadis, seperti perhatian pada syarat-syarat kesahihan hadis lainnya. Juga diketahui bahwa para ulama menggunakan kata ‘illah pada pengertian bahasanya, yaitu sesuatu yang masuk kepada sesuatu yang lain, lalu menyebabkan yang dimasukinya itu menjadi berubah. ‘Illah biasa dimaknai sebagai penyakit, karena jika penyakit masuk ke dalam tubuh, maka tubuh akan berubah menjadi lemah sama dengan ‘illah jika masuk ke dalam suatu hadis, maka akan mengubah kualitas dari hadis itu menjadi lemah. Cara untuk mengetahui adanya ‘illah dalam hadis ini adalah dengan melakukan mu‘āraḍah, yaitu pencocokan konsep yang menjadi muatan pokok setiap matan hadis agar tetap terpelihara kebertautan dan keselarasan antarkonsep dengan dalil syariat yang lain, yaitu dengan petunjuk eksplisit al-Quran, hadis yang lain, pengetahuan kesejarahan dan penalaran akal sehat.

Interlinked. Machines and humans facing the 10101 century

Robotics and artificial intelligence are two scientific research fields that receive considerable attention from the media and, consequently, from society. Unfortunately, many advances are reported to the general public in sensationalist (or even alarmist) terms, leading to false hopes or unjustified fears, and taking the focus from other key points. For instance, recent successes in artificial intelligence, amplified by the media, are the cause of a mistaken perception of this discipline’s state of the art. The reality is that artificial intelligence is still far from achieving many high-level cognitive skills; particularly, common sense reasoning.

A Natural Logic System for Large Knowledge Bases

This paper describes principles and structure for a software system that implements a dialect of natural logic for knowledge bases. Natural logics are formal logics that resemble stylized natural language fragments, and whose reasoning rules reflect common-sense reasoning. Natural logics may be seen as forms of extended syllogistic logic. The paper proposes and describes realization of deductive querying functionalities using a previously specified natural logic dialect called Natura-Log. In focus here is the engineering of an inference engine employing as a key feature relational database operations. Thereby the inference steps are subjected to computation in bulk for scaling-up to large knowledge bases. Accordingly, the system eventually is to be realized as a general-purpose database application package with the database being turned logical knowledge base.

FULL-FLEDGED SEMANTIC ANALYSIS AS A TOOL FOR RESOLVING TRIANGLE-COPA SOCIAL SCENARIOS

Text interpretation often requires common sense knowledge and reasoning. A convenient tool for developing methods of common sense reasoning are special sets of challenge problems whose interpretation requires sophisticated reasoning. An interesting example is a recently published data set called Triangle Choice of Plausible Alternatives (Triangle-COPA), which contains 100 multiple-choice problems that test the interpretation of social scenarios. Each problem includes a statement and two alternatives. The task is to identify the more plausible alternative. For processing Triangle-COPA data we use SemETAP, a general purpose semantic analyzer. We implement the full scenario of NL understanding starting from NL texts and not from manually composed simplified logical formulas, which is a common practice in logic-based approaches to common sense reasoning. We produce Enhanced Semantic Structures of the statement and both alternatives and check which alternative manifests more semantic agreement with the statement in terms of inferences.