Cfp: RADIO AI 2018: An Invitation to Talk About Your Work, Ideas, Thoughts

September 4, 2017 1:14 AM
RADIO AI - Call for Participation This invitation is extended across all the subfields of AI. The purpose of the RADIO AI project is to help educate the public and other professions about AI, with a crowd sourced collective of podcasts by people who work on AI. Submit podcasts by email to Submissions are in .mp3 or .wav. See for examples.

We hope to hear from many individuals who are passionate about their work, and who wish to counter the AI fear by educating the public with lighthearted informative podcasts. Many of the people who are creating the fear of AI do not actually work in AI.

Our goal is to to create an easy to access collective of  short informative lectures by the people who work in AI - academia, business, finance, healthcare, inventors, and programmers. The lectures can be as short as 3 minutes or as long as 10.  Some of you might wish to do a series of podcasts.  


Intent to submit November 1, 2017

Draft podcasts due: Dec. 12, 2017

Final podcasts due: Jan 12, 2017

Audience: Please gage the audience as either teenage or in another field of study that is non-technical.

Travel/hotel:   There is no travel or hotel reservation required. All you need is some peace and quiet, a microphone and your inspired thinking.

Contact: Dr. C. Mason


There are 2 example podcasts on the site       We can upload your podcast as soon as it is received and reviewed.Topics related to AI include - history of AI, rule based systems, software agents, robots, machine learning,  overview of AI, and applications - healthcare, legal, transportation, energy, environment, etc.  All topics related to AI are welcome.  Also, if you're working in this field and are aware of the social changes that will or already havebeen taking place, we welcome your insights for the public.  We hope this will be fun for you and that by doing this, it will make you happy. It will also be a service to ourselves and the public.


MOdeling LAnguages: The Portal on Models in Software Engineering

August 14, 2017 11:43 AM

Jordi Cabot delivered a very relevant and interesting keynote, The Secret Life of Rules in Software Engineering, at RuleML+RR 2017. His arguments for the desideratum that "rules must become first-class citizens in any software development project" are completely convincing in the RuleML+RR community and should increasingly appeal to (new?) people in SE. Likewise, most RuleML and RR colleagues would probably subscribe to the step-wise-transformation approach from model/spec to code that he explained. He also sketched how schema discovery over big data can infer explicit rules for use in data processing, Moreover, he emphasized the role of OCL in constraint/rule-based modeling, referring to a paper integrating OCL, RuleML and SWRL from the current community.

Jordi is maintaining the portal MOdeling LAnguages, which promotes the use of (rule-based) software models and engineering principles in software engineering. We should use the opportunity of Jordi’s keynote and portal for a fresh look into OCL and into RuleML's connections to it (including for the object-relational PSOA RuleML). The discussion between the communities has started at RuleML+RR 2017 in London. Please contribute to it, both on "MOdeling LAnguages" and "RuleML Blog & Social Mediazine".

Logic and AI – The Last 50 Years, Bob Kowalski, RuleMLRR2017

August 13, 2017 10:46 AM
A great talk about the origins and history of logic programming as well as its actual research challenges.

Bob Kowalski ( was a research fellow at the University of Edinburgh (1970–75) and has been at Imperial College London since 1975, attaining a chair in Computational logic in 1982 and becoming Emeritus Professor in 1999.

More at:

RuleML+RR2017: Call for Participation

July 3, 2017 9:42 AM
2017: International Joint Conference on Rules and Reasoning London 12-15 July 2017, the leading international joint conference in the field of rule-based reasoning, from foundations to technologies to applications.

The full program can be seen at

Keynotes and tutorial by academic and industrial experts. Conference dinner booked at the Royal Society.

Register at or at the door on Tuesday 11 July 2017 3-9 pm at Birkbeck, University of London, room MAL (main Building basement) B04. Meet and Greet, and wine and nibbles also on Tuesday 11 July 2017 3-9 pm in the same room.

Final Deadline: Work-in-Progress POSTERS: delegates (e.g., from industry) have the opportunity of presenting related Work-in-Progress Posters, optionally coming with demos, during the poster session, by sending PDFized poster files to Fariba Sadri by July 3rd, 2017, 3PM UK Time.

Call for Applications: The 13th Reasoning Web Summer School (RW 2017)

June 26, 2017 9:53 AM
The summer school takes place in London, U.K., July 7-11, 2017 and it is co-located with, DecisionCamp 2017, 11th International Rule Challenge

The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques which are of particular interest to Semantic Web and Linked Data applications. The school is primarily intended for postgraduate (PhD or MSc) students, postdocs, young researchers, and senior researchers wishing to learn about Reasoning on the Semantic Web and related issues. In 2017, the theme of the school is:

"Semantic Interoperability on the Web"

As in the previous years, lectures in the summer school will be given by a distinguished group of expert lecturers. Most lecturers will also be present for the duration of the school to interact and establish contacts with the students.

The summer school is co-located with RuleML+RR, a conference that joins the well-known RuleML and RR event series, DecisionCAMP 2017, and the 11th International Rule Challenge, hence, there will be a great opportunity for students to also attend some major events in the area. In addition, RuleML+RR will also include a Doctoral Consortium and students of RW are particularly encouraged to also apply to the Doctoral consortium of RuleML+RR.

Participants to the school will be delivered on request a certificate of attendance indicating the number of hours of lectures. With this certificate, some institutions may assign official credits for the PhD program.


  • Andrea Calì (Birkbeck University of London, U.K.) "Ontology querying: Datalog strikes back"
  • Thomas Eiter (Technical University of Wien, Austria) "Answer Set Programming with External Source Access"
  • Thomas Lukasiewicz (University of Oxford, U.K.) "Uncertainty Reasoning for the Semantic Web"
  • Marco Montali (Free University of Bolzano/Bozen, Italy) "Ontology-based Data Access for Log Extraction in Process Mining"
  • Axel Polleres (Vienna University of Economics & Business, Austria) "Challenges for Semantic Data Integration on the Web of Open Data"
  • Marie-Christine Rousset (University Grenoble-Alpes, Institut Universitaire de France) "Datalog revisited for reasoning in Linked Data"
  • Torsten Schaub (University of Potsdam, Germany and Inria, Bretagne Atlantique, Rennes, France) "A Tutorial on Hybrid Answer Set Solving"
  • Juan Sequeda (Capsenta, USA) "Integrating Relational Databases with the Semantic Web"
  • Giorgos Stamou (National Technical University of Athens, Greece) "Ontological query answering over semantic data"


A software agent controlling 2 robots arms in co-operating concurrent tasks

June 23, 2017 10:23 AM
Keith Leonard Clark ( is a Professor of Computer Science at Imperial College London, England. He earned a Ph.D. in 1980 from Queen Mary, University of London and since 1979, Keith Clark has had an academic position in the Department of Computing, Imperial College where he has been Professor of Computational Logic since 1987.

Abstract: The agent concurrently builds several different block towers as separate tasks. Each executes the same TeleoR rule program, and interacts with the same graphical Python simulator. The blocks are distributed over 3 tables, and each arm can only reach two tables, a home table and a shared table. The arms and tables are resources that are fairly shared by the tasks, without interference between tasks, without starvation of any task, and without deadlock. Frequently both arms are used in parallel.

The agent knows what a tower of blocks is, as it can do reasoning from percept facts sent to it from the simulator using relation defining rules in an LP+FP language QuLog. The percepts inform the agent if a block is directly on top of another block, or on a particular table, or held by one of the arms. Percept updates are sent by the simulator whenever blocks are moved, either by a simulated robotic arm controlled by the agent, or by me using the simulator’s GUI. The percepts are stored in the agent’s Belief Store, shared by all the tasks.

TeleoR programs are robust and opportunistic, as will be shown in the demo. If I undo a partially built tower the task T building it will try to reconstruct it as soon as it can acquire the arm and table resources needed. It I help, putting the next needed block B on T's partial tower, T will skip that action. It will instead uncover and place the next block B’ on top of B. Or it will pause if its tower is now complete. It will spring back into life if blocks are moved from that tower.

The tasks also try to help each other, making use of facts in the Belief Store that record the list of blocks of each tower building task. When clearing blocks on top of the next block B needed for its tower, a task T will place a removed block RB that will later be needed by another task OT, including T itself, in order to facilitate OT’s pickup of RB. If possible, T will place RB on a table, which T currently has as an acquired resource, so that OT can move RB to its destination partial tower using just one arm, and without the need to acquire the shared table as a resource. Minimising the number of times tasks need to use the shared table resource, maximises the parallel use of the arms. It therefore reduces the overall time needed to build all the towers.

The tasks also try to help each other, making use of facts in the Belief Store that record the list of blocks of each tower building task. When clearing blocks on top of the next block B needed for its tower, a task T will place a removed block RB that will later be needed by another task OT, including T itself, in order to facilitate OT’s pickup of RB. If possible, T will place RB on a table, which T currently has as an acquired resource, so that OT can move RB to its destination partial tower using just one arm, and without the need to acquire the shared table as a resource. Minimising the number of times tasks need to use the shared table resource, maximises the parallel use of the arms. It therefore reduces the overall time needed to build all the towers.

The demo will finish with a short video of a Baxter two armed robot concurrently building two real block towers with both help and hindrance from a person. The robot’s arms are moved in parallel whenever this can be done without risk of their clashing over a shared table area.

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Computer Science PhD grants @ KRDB - Univ. of Bozen-Bolzano Italy

June 21, 2017 5:07 PM
10 four-years grants are offered by the faculty of Computer Science of the Free University of Bozen-Bolzano in Italy for its PhD programme. Each grant amounts to 68,000 €; for research visits abroad the grant can increase up to 50%. Substantial extra funding is available for participation to international conferences, schools, workshops, research visits. The language of the PhD program is English.

 The deadline for applications is

3 July 2017 

For more info, the call, and applications look at:


The university is located in one of the most fascinating European regions, the Dolomites. This young university has already established itself as an important research institution, both in Italy and abroad.

According to the Times Higher Education World University Rankings 2017, the university is the tenth world’s best small university, is the fifth best among all the Italian Universities with computer science departments, and it is the second best young Italian University.

The KRDB Research Centre of the faculty ( is widely recognised as one of the internationally leading groups in knowledge representation research, with a synergy between foundational and application-oriented research. Among the various available PhD topics described in the call, the KRDB Research Centre is looking for PhD students interested in Conceptual Data Modelling and Ontology Design, Intelligent Information Access and Query processing, Information Integration, Semantic Technologies, Knowledge Representation, Computational Logic, Artificial Intelligence, foundations of Process-aware Information Systems.

To get in contact with the KRDB Research Centre and discuss about the opportunities of this call contact prof. Alessandro Artale at

Keynote Talk: Logic and AI – The Last 50 Years

June 21, 2017 1:22 PM
Bob Kowalski ( was a research fellow at the University of Edinburgh (1970–75) and has been at Imperial College London since 1975, attaining a chair in Computational logic in 1982 and becoming Emeritus Professor in 1999.

Fifty years ago in 1967, the research community working in Logic and AI was tiny. Everyone knew everyone else, and most communication between researchers was by word of mouth or correspondence. If you didn’t work in Stanford, MIT, Carnegie Mellon or Edinburgh, you were seriously disadvantaged.In 1967, Pat Hayes and I started our PhDs in Edinburgh. Bernard Meltzer was our Ph.D. supervisor, and John Alan Robinson, who developed resolution logic, was in Edinburgh on sabbatical. Bernard started the Journal of Artificial Intelligence in 1970, and co-edited the influential series of Machine Intelligence Workshop Proceedings with Donald Michie. The workshops attracted a wide range of researchers, mainly from the UK and USA, including Cordell Green and John McCarthy from Stanford. There was much excitement about Cordell’s application of resolution to a wide range of AI applications.The high hopes for resolution logic were challenged by researchers, including Marvin Minsky, Carl Hewitt, Gerald Sussman and Terry Winograd, at MIT. The resulting intellectual skirmishes between advocates of logical and procedural approaches to knowledge representation led to the development of logic programming and Prolog in 1972. My contributions to this development were greatly assisted by visits to Alain Colmerauer in Marseille.Over the subsequent years, we have seen the Fifth Generation Project with its focus on the application of logic programming to AI applications, the rise of the internet, and now big data and deep learning. But the fundamental challenge to Computer Science of the relationship between declarative (logical) and imperative (procedural) representations is still unresolved.Motivated by this challenge, Fariba Sadri and I are developing LPS (Logic-based Production System), as an imperative language with a logical interpretation. Programs include logic programs, interpreted as beliefs, and a logical reconstruction of production rules, interpreted as goals. Computation executes actions, to satisfy a global imperative of making the goals true in a model of the world determined by the beliefs. There is an online prototype of LPS, developed in the CLOUT (Computational Logic for Use in Teaching) project, to teach logic and computing to children.

There are 5 Tutorials at

June 20, 2017 12:38 PM
Register with RuleML+RR2017 ( to meet tutorial authors Robert Kowalski, Fariba Sadri, Miguel Calejo, Jacob Feldman, Francesca A. Lisi, Benjamin Grosof, Michael Kifer, Paul Fodor, Fabrizio Riguzzi, and Riccardo Zese.

Decision Modeling with DMN and OpenRules (1h45m)

by Jacob Feldman

This tutorial with introduce major business decision modeling concepts in the Decision Model and Notation (DMN) standard – see We will demonstrate the practical use of DMN by implementing various decision models using an popular open source Business Rules and Decision Management system “OpenRules” ( We will start with creation and testing of a simple decision model oriented to business people only. Then we will explain how the tested decision models can be integrate with IT systems. Then we will develop several more complex enough decision models demonstrating the power and applicability of different decision modeling constructs. We will end up with development of custom decisioning constructs that go beyond the DMN standard but support real-world decision modeling needs. All demonstrated decision models will be actually executed and analyzed with the audience during the presentation.

Logic-based Rule Learning for the Web of Data (1h45m)

by Francesca A. Lisi

The tutorial introduces to Inductive Logic Programming (ILP), being it a major logic-based approach to rule learning, and surveys extensions and applications of ILP to the Web of Data.

How to do it with LPS (Logic-Based Production System) (3h00m)

by Robert Kowalski, Fariba Sadri, Miguel Calejo

CLOUT is an open-source, web-based prototype of the computer language LPS (Logic-based Production System), implemented in SWISH. LPS includes both logic programming, which underpins the computer language Prolog, and a logical reconstruction of production systems, which are, arguably, the most popular computational model of human thinking. LPS fills the gap between imperative and logical languages, by viewing computation as generating state-transforming actions, to make goals, represented in logical form, true. This combination of logic and change of state makes LPS not only a programming, database, and AI knowledge representation and problem-solving language, but also a scaled-down model of human thinking.

The tutorial will present LPS by means of the web-based implementation, CLOUT, using such examples from programming, databases and AI, as sorting, dining philosophers, bank account maintenance, map colouring, the blocks world, and the prisoner’s dilemma. It will demonstrate the relationship between LPS and such other approaches to computing as production systems, reactive systems, abstract state machines and BDI agent languages. Moreover, it will show the close relationship between LPS, MetaTem, Transaction Logic and Abductive Logic Programming (ALP).

Rulelog: Highly Expressive Semantic Rules with Scalable Deep Reasoning (3h30m)

by Benjamin Grosof, Michael Kifer, Paul Fodor

In this half-day tutorial, we cover the fundamental concepts, key technologies, emerging applications, recent progress, and outstanding research issues in the area of Rulelog, a leading approach to fully semantic rule-based knowledge representation and reasoning (KRR). Rulelog matches well many of the requirements of cognitive computing. It combines deep logical/probabilistic reasoning tightly with natural language processing (NLP), and complements machine learning (ML). Rulelog interoperates and composes well with graph databases, relational databases, spreadsheets, XML, and expressively simpler rule/ontology systems – and can orchestrate overall hybrid KRR. Developed mainly since 2005, Rulelog is much more expressively powerful than the previous state-of-the-art practical KRR approaches, yet is computationally affordable. It is fully semantic and has capable efficient implementations that leverage methods from logic programming and databases, including dependency-aware smart cacheing and a dynamic compilation stack architecture.

Probabilistic Inductive Logic Programming on the Web (1h30m)

by Fabrizio Riguzzi, Riccardo Zese

Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning algorithms for PLP, SLIPCOVER. Moreover, an online version was recently developed, cplint on SWISH, that allows users to experiment with the system using just a web browser. In this tutorial, we illustrate cplint on SWISH concentrating on structure learning with SLIPCOVER.

Keynote Talk: Machine Learning and Decision Optimization

June 20, 2017 11:58 AM

Many technologies can be used to help or automate decision making. Business rules and decision management is a prominent category, but there are others, including machine learning and optimization. While seemingly unrelated, machine learning, decision optimization, and business rules can be used in combination to provide unprecedented accuracy in decision making. We will discuss several compelling examples of such combination used in various industries in the first part of this talk.

Then, we will review machine learning and decision optimization in more details. Both are based on mathematical optimization algorithms: a business problem is cast into an optimization problem. However, the problem to be optimized is very different. For machine learning, the problem is to minimize the difference between predictions and observed target values. For decision optimization, the problem is to minimize some operations cost. We will also show how data scientists approach machine learning and decision optimization in practice via the use of declarative modeling tools.


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