Rick Valenzano @RickValenzano
Assistant Professor at Toronto Metropolitan University. General admirer of sport, music, film, and whimsy. rickvalenzano.com Toronto, Ontario Joined November 2011-
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We cover topics such as automated planning, search methods, experimental design, etc. Could be interesting to a variety of @CompSciGradTMU students, as well as @FEAStorontometu students, especially those doing robotics, optimization, and operations research
Excited to be teaching my grad course on heuristic search at @TorontoMetSci this fall! See sites.google.com/torontomu.ca/r… for more details. Note, the course was rescheduled from the winter to the fall. @CompSciGradTMU @TMUgraduate @FEAStorontometu @researchTMU
@c_a_i_a_c Congratulations @SheilaMcIlraith
This year we are happy to announce two recipients of the CAIAC Lifetime Achievement Award: Dr. Sheila McIlraith (U. Toronto) and Dr. Ghorbani Ali (U. New Brunswick). These awards recognize outstanding research excellence in AI throughout their careers. caiac.ca/en/lifetime-ac…
Excited to again be teaching my grad course on Heuristic Search at @TMU_CS . If you are interested, you can find more info at bit.ly/41WV4Av @TMUgraduate @TorontoMetSci @CompSciGradTMU Could also be interesting for Operations Research students in @FEAStorontometu
I am looking for MSc and PhD students to join my team! If you are interested in heuristic search, planning, and the intersection of these topics in RL, consider applying at torontomu.ca/cs/graduate/ . More information about me at cs.torontomu.ca/~rick.valenzano
We have postdoc positions available at U of T’s theory group. I, in particular, would love to have a differential privacy postdoc. Lots of amazing people working in TCS, ML, privacy in the area! Here is the ad cs.toronto.edu/theory/positio…. Feel free to apply or share
But the "heuristic used by Dijkstra's" satisfies that condition. The bi-directional paper is here: webdocs.cs.ualberta.ca/~nathanst/pape… The 1985 paper is here: dl.acm.org/doi/10.1145/38…
And says nothing about those with an evaluation equal to the length of the optimal path. The A*-based work also typically assumes a "consistent" heuristic, which is a triangle inequality on how the heuristic function changes from vertex to vertex
There are some differences in setting. Dijkstra finds a path from an input s to ALL other vertices, whereas A* focuses on finding one path from s to a single vertex (typically). This is why the A* work focuses on "must expand" vertices.
@thesasho This was first shown by Dechter and Pearl in 1985. There is more recent work by @nathansttt et al. that I think cleans it up a bit and extends the result to bi-directional search.
@thesasho This was first shown by Dechter and Pearl in 1985. There is more recent work by @nathansttt et al. that I think cleans it up a bit and extends the result to bi-directional search.
Interesting. I am going to have to take a look. I'm curious how it extends to A*, for which we know that any "equally informed" algorithm B (ie. with the same heuristic), A* will be at least as fast as B (aside from tie-breaking). Dijkstra's after all is just A* with no heuristic
This looks really cool! With the right priority queue, Dijkstra's algorithm is universally optimal: for any graph G and any other algorithm A, there exist weights on the graph edges for which A would make at least as many comparisons as Dijkstra's.
@levilelis @IJCAIconf @JAIR_Editor @RToroIcarte @SheilaMcIlraith Good day for both of us, I see. Congratulations to you too!
Thrilled to announce that our JAIR paper on Reward Machines (jair.org/index.php/jair…) has won the 2023 IJCAI-JAIR Best Paper Prize @IJCAIconf @JAIR_Editor Joint work with @RToroIcarte Toryn Klassen and @SheilaMcIlraith
Excited to announce our paper on Learning Reward Machines from experience for partially observable reinforcement learning has been published as a pre-proof in AIJ. Work with @RToroIcarte @mcilraith and other : doi.org/10.1016/j.arti…
We are still accepting grad applications at @TMU_CS for Canadian students. I myself have a few positions open for people interested in heuristic search, planning, and combining those topics with RL. Check out my work at rickvalenzano.com if you are interested!
Looking forward to again be teaching a grad class on Heuristic Search and AI at @TorontoMet . If you are a student interested in attending, you can find out more at bit.ly/3bZRzUj @CompSciGradTMU
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