computer science

Leslie P. Kaelbling

Leslie P. Kaelbling is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Leslie P. Kaelbling rather than just read about it. In short: Leslie Pack Kaelbling is an American roboticist and the Panasonic Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. She is widely recognized for adapting partially observable Markov decision processes from operations research for application in artificial intelligence and robotics.

Key takeaways

Leslie P. Kaelbling belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Leslie P. Kaelbling to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Leslie P. Kaelbling from memory before moving on to harder problems.

Reference excerpt

Leslie Pack Kaelbling is an American roboticist and the Panasonic Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. She is widely recognized for adapting partially observable Markov decision processes from operations research for application in artificial intelligence and robotics. Kaelbling received the IJCAI Computers and Thought Award in 1997 for applying reinforcement learning to embedded control systems and developing programming tools for robot navigation. In 2000, she was elected as a Fellow of the Association for the Advancement of Artificial Intelligence.

Career Kaelbling received an A. B. in Philosophy in 1983 and a Ph.D. in Computer Science in 1990, both from Stanford University. During this time she was also affiliated with the Center for the Study of Language and Information. She then worked at SRI International and the affiliated robotics spin-off Teleos Research before joining the faculty at Brown University. She left Brown in 1999 to join the faculty at MIT. Her research focuses on decision-making under uncertainty, machine learning, and sensing with applications to robotics.

Journal of Machine Learning Research In the spring of 2000, she and two-thirds of the editorial board of the Kluwer-owned journal Machine Learning resigned in protest to its pay-to-access archives with simultaneously limited financial compensation for authors. Kaelbling co-founded and served as the first editor-in-chief of the Journal of Machine Learning Research, a peer-reviewed open access journal on the same topics which allows researchers to publish articles for free and retain copyright with its archives freely available online. In response to the mass resignation, Kluwer changed their publishing policy to allow authors to self-archive their papers online after peer-review. Kaelbling responded that this policy was reasonable and would have made the creation of an alternative journal unnecessary, but the editorial board members had made it clear they wanted such a policy and it was only after the threat of resignations and the actual founding of JMLR that the publishing policy finally changed.

Selected works Reinforcement Learning: A Survey (LP Kaelbling, ML Littman, AW Moore). Journal of Artificial Intelligence Research (JAIR) 4 (1996) 237-285. A highly cited survey on the field of reinforcement learning. Planning and acting in partially observable stochastic domains (LP Kaelbling, ML Littman, AR Cassandra). Artificial Intelligence 101 (1), 99-134. Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation (AR Cassandra, LP Kaelbling, JA Kurien). Intelligent Robots and Systems (2) 963-972. The synthesis of digital machines with provable epistemic properties (SJ Rosenschein, LP Kaelbling). Proceedings of the 1986 Conference on Theoretical Aspects of Reasoning about Knowledge, 83-98. Practical reinforcement learning in continuous spaces (WD Smart, LP Kaelbling). 2000 International Conference on Machine Learning (ICML), 903-910. Hierarchical task and motion planning in the now (LP Kaelbling, T Lozano-Pérez). 2011 IEEE International Conference on Robotics and Automation (ICRA), 1470-1477.

References

Worked examples

Example 1 — a first encounter with Leslie P. Kaelbling

Start with the simplest possible case. Write down what Leslie P. Kaelbling claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Leslie P. Kaelbling before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Leslie P. Kaelbling ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Leslie P. Kaelbling

In research: Leslie P. Kaelbling appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Leslie P. Kaelbling in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Leslie P. Kaelbling is common in secondary-school and first-year university syllabi. It links to neighbouring topics 21st-century American women, American artificial intelligence researchers, American computer science educators, so understanding it makes those chapters shorter.
In everyday life: Look for Leslie P. Kaelbling outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Leslie P. Kaelbling in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Leslie P. Kaelbling means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Leslie P. Kaelbling out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Leslie P. Kaelbling in simple terms?

Why does Leslie P. Kaelbling matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Leslie P. Kaelbling?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Leslie P. Kaelbling.