"I think the best hope for human-level AI is logical AI, based on the formalizing of commonsense knowledge and reasoning in mathematical logic. Formalizing common sense requires extensions to mathematical logic including nonmonotonic reasoning and extensive reification, e.g., of concepts and also contexts. The reifications require appropriate reflection schemas."
– from The Future of AI—A Manifesto by John McCarthy. AI Magazine 26(4), (2005).
As artificial intelligence (AI) techniques advance, they are beginning to automate tasks that, until recently, only humans could perform--tasks such as translating text from one language to another or making medical diagnoses. It seems only logical to turn that computer power on computers themselves and use AI to automate programming. In fact, computer scientists are working on just that idea, using various AI techniques to develop new methods of automating the writing of code. "The ultimate goal of this is that you would have professional software engineers not actually write code anymore," says Chris Jermaine, a professor of computer science at Rice University in Houston, TX. Instead, the engineer would tell a computer what a piece of software should do, and the AI system would write the code, perhaps stopping along the way to pose questions to the engineer.
One of the fastest advancing areas of modern science is functional genomics. This science seeks to understand how the complete complement of molecular components of living organisms (nucleic acid, protein, small molecules, and so on) interact together to form living organisms. Functional genomics is of interest to AI because the relationship between machines and living organisms is central to AI and because the field is an instructive and fun domain to apply and sharpen AI tools and ideas, requiring complex knowledge representation, reasoning, learning, and so on. This article describes two machine learning (inductive logic programming [ILP])-based approaches to the bioinformatic problem of predicting protein function from amino acid sequence. The first approach is based on using ILP as a way of bootstrapping from conventional sequence-based homology methods.
Doug Lenat has worked in diverse parts of AI – natural language understanding and generation, automatic program synthesis, expert systems, machine learning, etc. – for going on 40 years now, just long enough to dare to write this article. His 1976 Stanford PhD thesis, AM, demonstrated that creative discoveries in mathematics could be produced by a computer program (a theorem proposer, rather than a theorem prover) guided by a corpus of hundreds of heuristic rules for deciding which experiments to perform and judging "interestingness" of their outcomes. That work earned him the IJCAI Computers and Thought Award, and sparked a renaissance in machine learning research. Dr. Lenat was on the CS faculty at CMU and Stanford, was one of the founders of Teknowledge, and was in the first batch of AAAI Fellows. He worked with Bill Gates and Nathan Myhrvold to launch Microsoft Research Labs, and to this day he remains the only person to have served on the technical advisory boards of both Apple and Microsoft.
Scientists say the reopening of schools in England, both primary and secondary, is'unlikely' to lead to a second wave of coronavirus infections. The gradual reopening of schools, starting with primary schools, wouldn't drive the average coronavirus transmission rate above one, infectious disease experts claim. Their mathematical modelling study shows that the impact of less social distancing on the part of adults would in fact be more likely to cause a'second wave'. As part of a phased return to schools in effect since Monday, the government is allowing pupils in reception, year one and year six to return to classes. While this policy could slightly raise the'R' number – the average amount of people that one infection person would pass the virus on to – it's unlikely to push it above one, the research team claims.
Markov decision processes are of major interest in the planning community as well as in the model checking community. But in spite of the similarity in the considered formal models, the development of new techniques and methods happened largely independently in both communities. This work is intended as a beginning to unite the two research branches. We consider goal-reachability analysis as a common basis between both communities. The core of this paper is the translation from Jani, an overarching input language for quantitative model checkers, into the probabilistic planning domain definition language (PPDDL), and vice versa from PPDDL into Jani. These translations allow the creation of an overarching benchmark collection, including existing case studies from the model checking community, as well as benchmarks from the international probabilistic planning competitions (IPPC). We use this benchmark set as a basis for an extensive empirical comparison of various approaches from the model checking community, variants of value iteration, and MDP heuristic search algorithms developed by the AI planning community. On a per benchmark domain basis, techniques from one community can achieve state-ofthe-art performance in benchmarks of the other community. Across all benchmark domains of one community, the performance comparison is however in favor of the solvers and algorithms of that particular community. Reasons are the design of the benchmarks, as well as tool-related limitations. Our translation methods and benchmark collection foster crossfertilization between both communities, pointing out specific opportunities for widening the scope of solvers to different kinds of models, as well as for exchanging and adopting algorithms across communities.
In this work we describe preferential Description Logics of typicality, a nonmonotonic extension of standard Description Logics by means of a typicality operator T allowing to extend a knowledge base with inclusions of the form T(C) D, whose intuitive meaning is that "normally/typically Cs are also Ds". This extension is based on a minimal model semantics corresponding to a notion of rational closure, built upon preferential models. We recall the basic concepts underlying preferential Description Logics. We also present two extensions of the preferential semantics: on the one hand, we consider probabilistic extensions, based on a distributed semantics that is suitable for tackling the problem of commonsense concept combination, on the other hand, we consider other strengthening of the rational closure semantics and construction to avoid the so called "blocking of property inheritance problem".
Data science is a cornerstone of current business practices. A major obstacle to its adoption is that most data analysis techniques are beyond the reach of typical end-users. Spreadsheets are a prime example of this phenomenon: despite being central in all sorts of data processing pipelines, the functionality necessary for processing and analyzing spreadsheets is hidden behind the high wall of spreadsheet formulas, which most end-users can neither write nor understand [Chambers and Scaffidi, 2010]. As a result, spreadsheets are often manipulated and analyzed manually. This increases the chance of making mistakes and prevents scaling beyond small data sets. Lowering the barrier to entry for specifying and solving data science tasks would help ameliorating these issues. Making data science tools more accessible would lower the cost of designing data processing pipelines and taking datadriven decisions. At the same time, accessible data science tools can prevent non-experts from relying on fragile heuristics and improvised solutions. The question we ask is then: is it possible to enable nontechnical end-users to specify and solve data science tasks that match their needs?
We consider three modern roles for logic in artificial intelligence, which are based on the theory of tractable Boolean circuits: (1) logic as a basis for computation, (2) logic for learning from a combination of data and knowledge, and (3) logic for reasoning about the behavior of machine learning systems.
Intuitively, locally finiteness means that the refinement operator is computable, completeness means we can generate, by refinement of a, any element of G related to a given element g 1 by the order relation, and properness means that a refinement operator does not generate elements which are equivalent to the element being refined. When a refinement operator is locally finite, complete and proper, we say that it is ideal. Notice that all the subsumption relations presented above satisfy the reflexive 2 and transitive 3 properties. Therefore, the pair (G,), where G is the set of all DLGs given a set of labels L, and is any of the subsumption relations defined above is a quasi-ordered set. Thus, this opens the door to defining refinement operators for DLGs. Intuitively, a downward refinement operator for DLGs will generate refinements of a given DLG by either adding vertices, edges, or by making some of the labels more specific, thus making the graph more specific. In the following subsections, we will introduce a collection of refinement operators for connected DLGs, and discuss their theoretical properties. A summary of these operators is shown in Table 1, where we show that under the object-identity constraint, all the refinement operators presented in this document are ideal. If we do not impose object-identity, then the operators are locally complete and complete, but not proper.