How does one deal with the unexpected? Our world is full of surprises and we humans are often able to correctly identify a problem and respond appropriately. Consider a new driver encountering their first traffic circle; a student experiencing a hard drive failure in the middle of an assignment; an unexpected question being asked during a job interview. In situations where we have a goal (i.e., reach a destination or submit a completed assignment), we may need to alter our original plan when the unexpected occurs. Could we enable autonomous artificial intelligent agents to do the same?
A long standing area of artificial intelligence is the field of automated planning. The traditional planning problem is to generate a sequence of actions given a concrete, specific goal (e.g., I will be home at dinnertime) and a set of specific actions (e.g., drive-car, fill-gas-tank, walk, etc). Generating plans that are hopefully efficient and optimal from start to finish under different circumstances (e.g., delayed effects) is an active area of research. After a plan has been generated, and during the execution of the plan, the environment may change. For example, a robot retrieving packages in a warehouse may discover it has dropped its package. Or perhaps another robot has broken down due to a hardware failure and is blocking the path of this robot. How can a robot (or any A.I. agent) know something unexpected has happened without knowing all possible future failures?
Fundamental research on autonomy aims to find general approaches to solve this problem. One approach is to generate expectations: facts that should be true during different stages of a plan's execution. When an expectation is violated, a discrepancy occurs between the expected and perceived facts. A new trend in autonomy is to include goal reasoning capabilities. In the event of a failure, the original goal may no longer be warranted. Perhaps robust autonomous agents need to generate and change their goals in response to a changing environment.
Autonomous systems still have a long way to go and open research questions on autonomous systems remain. Funding agencies consistently seek new research on autonomy for diverse operations ranging from cybersecurity to military and vehicular autonomy. What will autonomous systems be like in the future? Will we achieve autonomous agents that can handle any situation they encounter?
- Dustin Dannenhauer
The Festo Bionic Learning Network has released it latest biomimicry innovations to support ongoing research in engineering, manufacturing, and materials science drawn from natural systems. The biological model for the BionicWheelBot is the flic-flac spider Cebrennus rechenbergi) -- a species that lives in the Erg Chebbi desert on the edge of the Sahara. Just like living flic-flac spiders, the Festo BionicWheelBot propels itself with a tripod gait using six of its eight legs to walk. To start rolling, the BionicWheelBot bends three legs on each side of its body to form a wheel. Two lower-middle legs are folded up during walking then extend and push the rolled-up spider off the ground to propel it forward.
You may be in the best place to live in Colorado, but perhaps you want to travel beyond your home. You may want to travel in the same comfort as what you have in your home in Colorado. Artificial intelligence may help you to have a better and more easy vacation with the comforts of home more accessible to you with the right travel package. Artificial intelligence completely revolutionizes and improves the technology available for trip planning. There is some concern within many industries that artificial intelligence will do away with more jobs.
A former top U.S. Defense Department official said Tuesday he was "alarmed" by a recent decision by Alphabet Inc.'s Google to withdraw from work on a department initiative applying artificial intelligence tools to analyzing drone footage. "I fully agree that it might wind up with us taking a shot, but it could easily save lives," said Deputy Defense Secretary Robert O. Work, who started the initiative known as "Project Maven." "I believe the Google employees created an enormous moral hazard for themselves." More than 4,000 Google employees signed a petition calling for the cancellation of the Project Maven contract, citing Google's history of avoiding military work and worries about autonomous weapons. The company has since pledged not to use its AI for weapons, illegal surveillance and technologies that cause "overall harm," while saying it will continue to pursue government work in other areas.
We present a general constraint-based encoding for domain-independent task planning. Task planning is characterized by causal relationships expressed as conditions and effects of optional actions. Possible actions are typically represented by templates, where each template can be instantiated into a number of primitive actions. While most previous work for domain-independent task planning has focused on primitive actions in a state-oriented view, our encoding uses a fully lifted representation at the level of action templates. It follows a time-oriented view in the spirit of previous work in constraint-based scheduling. As a result, the proposed encoding is simple and compact as it grows with the number of actions in a solution plan rather than the number of possible primitive actions. When solved with an SMT solver, we show that the proposed encoding is slightly more efficient than state-of-the-art methods on temporally constrained planning benchmarks while clearly outperforming other fully constraint-based approaches.
CASP is an extension of ASP that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.
Fully observable non-deterministic (FOND) planning is becoming increasingly important as an approach for computing proper policies in probabilistic planning, extended temporal plans in LTL planning, and general plans in generalized planning. In this work, we introduce a SAT encoding for FOND planning that is compact and can produce compact strong cyclic policies. Simple variations of the encodings are also introduced for strong planning and for what we call, dual FOND planning, where some non-deterministic actions are assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial). The resulting FOND planners are compared empirically with existing planners over existing and new benchmarks. The notion of "probabilistic interesting problems" is also revisited to yield a more comprehensive picture of the strengths and limitations of current FOND planners and the proposed SAT approach.
With the rapid development of deep learning, deep reinforcement learning (DRL) began to appear in the field of resource scheduling in recent years. Based on the previous research on DRL in the literature, we introduce online resource scheduling algorithm DeepRM2 and the offline resource scheduling algorithm DeepRM_Off. Compared with the state-of-the-art DRL algorithm DeepRM and heuristic algorithms, our proposed algorithms have faster convergence speed and better scheduling efficiency with regarding to average slowdown time, job completion time and rewards.
FORT WORTH, Texas – American Airlines says a regional affiliate should run close to a normal operation Thursday after canceling 2,750 flights in the past week because of a computer problem. Spokeswoman Katie Cody said PSA Airlines stabilized its computer systems but faced delays getting planes and crews back in place. Based in Dayton, Ohio, PSA is owned by American and operates many American Eagle regional flights, especially in Charlotte, North Carolina. Cody said American has been rebooking stranded passengers on American and other airlines since disruptions started June 14. She said there was a hardware problem in computers used to run crew-scheduling applications.
Within the span of a month during the summer of 2016, two of the top four U.S. airlines suffered crippling IT failures. Delta Air Lines (NYSE:DAL) and Southwest Airlines (NYSE:LUV) were each forced to cancel thousands of flights during the peak season, leading to lost revenue and reputational damage. This article originally appeared in the Motley Fool. The summer 2018 peak season is just getting started, but there has already been a major airline IT failure. In the past week, flight cancellations have rapidly mounted at American Airlines' (NASDAQ:AAL) regional subsidiary PSA Airlines, due to problems with the carrier's crew scheduling system.
Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI is planning a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. Task planning is one example where AI enables more efficient and flexible operation through an online automated adaptation and rescheduling of the activities to cope with new operational constraints and demands. In this paper we present SMarTplan, a task planner specifically conceived to deal with real-world scenarios in the emerging smart factory paradigm. Including both special-purpose and general-purpose algorithms, SMarTplan is based on current automated reasoning technology and it is designed to tackle complex application domains. In particular, we show its effectiveness on a logistic scenario, by comparing its specialized version with the general purpose one, and extending the comparison to other state-of-the-art task planners.