The requirement for autonomous robots to exhibit higher-level cognitive skills by planning and adapting in an ever-changing environment is indeed a great challenge for the AI community. Progress has been made in the automated planning community on refinement and repair of an agent's symbolic knowledge to do task planning in an incomplete or changing environmental model, but these advances up to now have not been transferred to real physical robots. This paper demonstrates how a physical robot can be capable of adapting its symbolic knowledge of the environment, by using experiences in robot action execution to drive knowledge refinement and hence to improve the success rate of the task plans the robot creates. To implement more robust planning systems, we propose a method for refining domain knowledge to improve the knowledge on which intelligent robot behavior is based. This architecture has been implemented and evaluated using a NAO robot. The refined knowledge leads to the future synthesis of task plans which demonstrate decreasing rates of failure over time as faulty knowledge is removed or adjusted.

Australian universities are split on whether to adopt a new tool which claims to detect AI-generated plagiarism with a near-perfect success rate, citing concerns over out-of-date models and the minimal notice the sector was given to assess the issue. Turnitin's detection tool, launched this month, cites a 98% efficacy rate at picking up the "high probability" of AI. Of almost a dozen universities who responded to Guardian Australia, the University of Melbourne, the University of New South Wales and Western Sydney University have adopted the tool and several were considering integrating it into their detection programs. But others said the Turnitin tool was rushed and raised concerns over its efficacy. Deakin University associate professor in digital learning, Trish McCluskey, said despite Turnitin's alleged high efficiency rate, it hadn't had the opportunity to test the claim prior to the public release of the tool.

This paper is an experience report on the results of an industry-led collaborative project aimed at automating the control of traffic flow within a large city centre. A major focus of the automation was to deal with abnormal or unexpected events such as roadworks, road closures or excessive demand, resulting in periods of saturation of the network within some region of the city. We describe the resulting system which works by sourcing and semantically enriching urban traffic data, and uses the derived knowledge as input to an automated planning component to generate light signal control strategies in real time. This paper reports on the development surrounding the planning component, and in particular the engineering, configuration and validation issues that arose in the application. It discusses a range of lessons learned from the experience of deploying automated planning in the road transport area, under the direction of transport operators and technology developers.

It was the kind of message Connor McCluskey loves to find in his inbox. As a member of the product innovation team at FirstEnergy Corp. -- an electric utility serving 6 million customers from central Ohio to the New Jersey coast -- his job is to find technologies that open new revenue streams or cut costs. In the email, Chris Ricciuti, the founder of Noteworthy AI, explained his ideas for using edge computing to radically improve how utilities track their assets. For FirstEnergy, those assets include tens of millions of devices mounted on millions of poles across more than 269,000 miles of distribution lines. Ricciuti said his startup aimed to turn every truck in a utility's fleet into a smart camera that takes pictures of every pole it passes.

One approach to the problem of formulating domain models for planning is to learn the models from example action sequences. The LOCM system demonstrated the feasibility of learning domain models from example action sequences only, with no observation of states before, during or after the plans. LOCM uses an object-centred representation, in which each object is represented by a single parameterised state machine. This makes it powerful for learning domains which fit within that representation, but there are some well-known domains which do not. This paper introduces LOCM2, a novel algorithm in which the domain representation of LOCM is generalised to allow multiple parameterised state machines to represent a single object. This extends the coverage of domains for which an adequate domain model can be learned. The LOCM2 algorithm is described and evaluated by testing domain learning from example plans from published results of past International Planning Competitions.