qualifier


ARIAC Qualifier 3 is now open

Robohub

We are happy to announce that Qualifier 3 is now open for the Agile Robotics for Industrial Automation Competition (ARIAC)! ARIAC is a simulation-based competition is designed to promote agility in industrial robot systems by utilizing the latest advances in artificial intelligence and robot planning. The goal is to enable industrial robots on the shop floors to be more productive, more autonomous, and to require less time from shop floor workers. You can learn more about the competition here. The top performing teams will be invited to present at a workshop held during IROS 2017 in Vancouver.


Here's why artificial intelligence isn't out to get us

#artificialintelligence

AI has a long way to go before people can or should worry about turning the world over to machines. Elon Musk's new plan to go all-in on self-driving vehicles puts a lot of faith in the artificial intelligence needed to ensure his Teslas can read and react to different driving situations in real time. AI is doing some impressive things--last week, for example, makers of the AlphaGo computer program reported that their software has learned to navigate the intricate London subway system like a native. Even the White House has jumped on the bandwagon, releasing a report days ago to help prepare the U.S. for a future when machines can think like humans. But AI has a long way to go before people can or should worry about turning the world over to machines, says Oren Etzioni, a computer scientist who has spent the past few decades studying and trying to solve fundamental problems in AI.


Here's why artificial intelligence isn't out to get us

PBS NewsHour

AI has a long way to go before people can or should worry about turning the world over to machines. Elon Musk's new plan to go all-in on self-driving vehicles puts a lot of faith in the artificial intelligence needed to ensure his Teslas can read and react to different driving situations in real time. AI is doing some impressive things--last week, for example, makers of the AlphaGo computer program reported that their software has learned to navigate the intricate London subway system like a native. Even the White House has jumped on the bandwagon, releasing a report days ago to help prepare the U.S. for a future when machines can think like humans. But AI has a long way to go before people can or should worry about turning the world over to machines, says Oren Etzioni, a computer scientist who has spent the past few decades studying and trying to solve fundamental problems in AI.


A Discourse Approach to Explanation Aware Knowledge Representation

AAAI Conferences

This study describes a discourse approach to explanation aware knowledge representation. It presents a reasoning model that adheres to argumentation as found in written discourse, intended for use in intelligent human-computer collaboration and inter-agent deliberation. The approach integrates the Toulmin model with Rhetorical Structure Theory and Perelman and Olbrechts-Tyteca's (1958) strategic forms of argumentative processes to define a set of constraints for governing argumentative interactions and formulating explanations in an ontologically normalized manner. Arguments, when satisfied, are instantiated into a dynamic rhetorical network that represents the system's model of the situation. Two modalities of instantiation are proposed. Inferential instantiation is used when a claim may be inferred from a ground, and synthetic instantiation is used for descriptive argumentation where both ground and claim must be satisfied for the argument to be instantiated.


FS94-04-002.pdf

AAAI Conferences

In this paper, I discuss a formalism that I devised and implemented to deal with complex instructions, in particular those containing Purpose Clauses. I argue that such formalism supports inferences that are an important pragmatic aspect of natural language, and that at the same time are related to surface reasoning, based on the syntactic structure of Natural Language; and moreover, that by using the kind of approach I propose, namely, first define linguistic terms and then use them in the part of the KB concerning the semantics of the domain, we can start bridging the gap between the two representation languages that the organizers of the symposium mention, the first used to capture the semantics of a sentence, the second used to capture general knowledge about the domain. Details on all the topics discussed here can be found in [Di Eugenio, 1993; Di Eugenio, 1994]. 2 Motivations for the representation language The characteristics of the formalism I propose derive from an analysis of an extensive corpus of Purpose Clauses, infinitival to constructions as in Do to do fl; as some of these characteristics stem from the inferences necessary to interpret Purpose Clauses, it is with such inferences that I will start. Interpreting Do to do/3, where fl describes the goal to be achieved, in computational terms amounts to: (la) use fl as an index into the KB; (lb) find a collection of methods A4z that achieve (lc) try to match a to an action 7t,j that appears as a component in /lz. These are typical plan recognition inferences, eg see [Wilensky, 1983; Pollack, 1986; Charniak, 1988; Litman and Allen, 1990]. In all the work on plan recognition I know of, with the exception of [Charniak, 1988], match in step (lc) is taken to mean that a is instance-of 7t,j. However, given the variability of NL action descriptions, we can't assume that the input description exactly matches the knowledge that an agent has about actions and their mutual relations: my research focuses on computing a more flexible match between a and 7t,j-The two kinds of discrepancy between input and stored action descriptions I have examined so far concern structural consistency, and expectations that may need to be satisfied for a certain relation T¢ to hold between a and 3'z,j.