"Planning is the process of generating (possibly partial) representations of future behavior prior to the use of such plans to constrain or control that behavior. The outcome is usually a set of actions, with temporal and other constraints on them, for execution by some agent or agents. As a core aspect of human intelligence, planning has been studied since the earliest days of AI and cognitive science. Planning research has led to many useful tools for real-world applications, and has yielded significant insights into the organization of behavior and the nature of reasoning about actions."
– Planning entry by Austin Tate in the MIT Encyclopedia of Cognitive Science.
As the March deadline for leaving the European Union approaches, councils and other local organisations are drawing up contingency plans to be put in place in the event of the UK exiting the EU without a deal. The possibility that a no-deal Brexit (ie where the UK immediately drops out of the EU single market and customs union) could lead to traffic jams, especially around ports, is one area being looked at. In a letter to MPs, Transport Secretary Chris Grayling said it was "possible that there could be some freight traffic disruption in Kent in the event of a no-deal, if additional customs checks were introduced in Calais, Coquelles and Dunkirk, where freight services disembark." Kent is home to the Port of Dover, which handles approximately 10,500 lorries a day, with the Channel Tunnel receiving a further 6,000 lorries daily. Most lorries transporting freight overseas are accustomed to rolling on a ferry one end and rolling off at the other - it's relatively easy, seamless and quick.
Logistics is something we traditionally associate with the trucking or the package delivery industry. In fact, a recent article in the Economist estimates that the delivery of 25 packages equals roughly 15 septillion (trillion trillion) possible routes. That's why many companies dealing in complicated webs of variables like this are turning to new technologies like artificial intelligence to help streamline and optimize their operations. What if we took the concepts behind shipping logistics and applied them to the healthcare space? Imagine a healthcare organization with multiple locations, each staffed with providers across multiple specialties--individuals who are not interchangeable--operating under a wide range of room availability and scheduling constraints.
AI and machine learning unmask previously hidden workforce data to make people-centric decisions. Artificial intelligence (AI) and machine learning will finally be woven into workforce management practices, revealing a treasure trove of data organisations have been collecting – but not using – for decades. With regular and digestible access to workforce data trends – like scheduling accuracy, absenteeism, overtime usage, and burnout – predictive analytics will shine, helping organisations head-off potential issues before they arise. Intelligent automation will also free up managers from admin-heavy tasks – like managing schedules, approving time-off requests, and shift changes – while encouraging data-driven decision-making to provide clarity between what is equal versus what is fair. Though, to harness analytical insights to make accurate, actionable decisions for specific employee and business goals, organisations must avoid a "one-size-fits-all" model.
I am a PhD student majoring in Computer Science at Arizona State University. I am a member of Yochan research group directed by Prof. Subbarao Kambhampati. Before joining ASU in 2015, I did my Master's at University of Southern California with a major in Computer Science. At USC, I worked on multi-agent path planning problems at IDM Lab while being supervised by Dr. T. K. Satish Kumar and on human-robot interaction related projects at Interaction Lab. If you'd like to contact me, please drop me a mail at anaghak at asu dot edu or find me on LinkedIn.
If Theresa May fails to get her deal through parliament in January, the prospect of the UK leaving the EU without a deal becomes more likely. Here, Guardian journalists examine what a no-deal Brexit could mean for the country, sector by sector. In a no-deal scenario, the rights of British nationals in Europe to work and reside there will fall away unless a member state has contingency plans in place. For this reason many Britons have taken the precaution of becoming citizens of the countries in which they are settled. In its latest no-deal planning paper, published this month, the European commission urged member states to take a "generous" approach to protect the rights of 1 million Britons living in the bloc.
Over the last few months, additional ferry contracts were awarded to French, Dutch and British companies. The contingency plans allow for almost 4,000 more lorries a week to come and go from other ports, including Plymouth, Poole, and Portsmouth. Lib Dem leader Sir Vince Cable called the move "complete madness". "The government has the power to stop "no deal" at any time but instead is spending millions on last minute contracts," he said. "The fact that this money is predominantly going to European companies is nothing short of ironic, reducing Britain to a laughing stock on the global stage."
British businesses have criticised politicians for focusing on in-fighting rather than preparing for Brexit, warning that there is not enough time to prepare for a no-deal scenario. With 100 days to go before the UK leaves the EU, the groups say firms have been "watching in horror" at the ongoing rows within Westminster. The cabinet met on Tuesday to ramp up preparations for a no-deal departure. But the groups say the idea that "no-deal" can be managed is not credible. In a joint statement, the British Chambers of Commerce, the Confederation of British Industry, manufacturers' organisation the EEF, the Federation of Small Businesses and the Institute of Directors said: "Businesses have been watching in horror as politicians have focused on factional disputes rather than practical steps that business needs to move forward. "The lack of progress in Westminster means that the risk of a no-deal Brexit is rising." The government said on Tuesday that it had sent letters to 140,000 businesses, urging them to trigger their no-deal contingency plans as appropriate. It will also distribute 100-page information packs on Friday. The five business groups, which represent hundreds of thousands of UK firms, said that because of a lack of progress, the government "is understandably now in a place where it must step up no-deal planning". But they say: "It is clear there is simply not enough time to prevent severe dislocation and disruption in just 100 days.
Real-time traffic signal control systems can effectively reduce urban traffic congestion but can also become significant contributors to congestion if poorly timed. Real-time traffic signal control is typically challenging owing to constantly changing traffic demand patterns, very limited planning time and various sources of uncertainty in the real world (due to vehicle detection or unobserved vehicle turn movements, for instance). SURTRAC (Scalable URban TRAffic Control) is a recently developed traffic signal control approach which computes delay-minimising and coordinated (across neighbouring traffic lights) schedules of oncoming vehicle clusters in real time. To ensure real-time responsiveness in the presence of turn-induced uncertainty, SURTRAC computes schedules which minimize the delay for the expected turn movements as opposed to minimizing the expected delay under turn-induced uncertainty. Furthermore, expected outgoing traffic clusters are communicated to downstream intersections. These approximations ensure real-time tractability, but degrade solution quality in the presence of turn-induced uncertainty. To address this limitation, we introduce TuSeRACT (Turn-Sample-based Real-time trAffic signal ConTrol), a distributed sample-based scheduling approach to traffic signal control. Unlike SURTRAC, TuSeRACT computes schedules that minimize expected delay over sampled turn movements of observed traffic, and communicates samples of traffic outflows to neighbouring intersections. We formulate this sample-based scheduling problem as a constraint program, and empirically evaluate our approach on synthetic traffic networks. We demonstrate that our approach results in substantially lower average vehicle waiting times as compared to SURTRAC when turn-induced uncertainty is present.
In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Weighted Partial Maximum Boolean Satisfiability (FD-SAT-Plan+) as well as Binary Linear Programming (FD-BLP-Plan+). Theoretically, we show that our SAT-based Bi-Directional Neuron Activation Encoding is asymptotically the most compact encoding in the literature and maintains the generalized arc-consistency property through unit propagation -- an important property that facilitates efficiency in SAT solvers. Experimentally, we validate the computational efficiency of our Bi-Directional Neuron Activation Encoding in comparison to an existing neuron activation encoding and demonstrate the effectiveness of learning complex transition models with BNNs. We test the runtime efficiency of both FD-SAT-Plan+ and FD-BLP-Plan+ on the learned factored planning problem showing that FD-SAT-Plan+ scales better with increasing BNN size and complexity. Finally, we present a finite-time incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings through simulated or real-world interaction.
In this report, we introduce an artificial dataset generator for Photo-realistic Blocksworld domain. Blocksworld is one of the oldest high-level task planning domain that is well defined but contains sufficient complexity, e.g., the conflicting subgoals and the decomposability into subproblems. We aim to make this dataset a benchmark for Neural-Symbolic integrated systems and accelerate the research in this area. The key advantage of such systems is the ability to obtain a symbolic model from the real-world input and perform a fast, systematic, complete algorithm for symbolic reasoning, without any supervision and the reward signal from the environment.