The Association for the Advancement of Artificial Intelligence (AAAI) held its 1998 Fall Symposium Series on 23 to 25 October at the Omni Rosen Hotel in Orlando, Florida. This article contains summaries of seven of the symposia that were conducted: (1) Cognitive Robotics; (2) Distributed, Continual Planning; (3) Emotional and Intelligent: The Tangled Knot of Cognition; (4) Integrated Planning for Autonomous Agent Architectures; (5) Planning with Partially Observable Markov Decision Processes; (6) Reasoning with Visual and Diagrammatic Representations; and (7) Robotics and Biology: Developing Connections.
Bayes's Theorem fundamentally is based on the concept of "validity of Beliefs". Reverend Thomas Bayes was a Presbyterian minster and a Mathematician who pondered much about developing the proof of existence of God. He came up with the Theorem in 18th century (which was later refined by Pierre-Simmon Laplace) to fix or establish the validity of'existing' or'previous' Beliefs in the face of best available'new' evidence. Think of it as a equation to correct prior beliefs based on new evidence. One of the popular example used to explain Bayes's Theorem is to detect if a patient has a certain disease or not.
In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation.
Government-funded artificial intelligence programs could soon be organized under a new effort by the General Services Administration. GSA earlier this month created the Data Federation, a site that intends to coordinate the disparate existing data-related efforts at various agencies by sharing standards, case studies and reusable tech tools. On Monday, GSA plans to announce a new community of practice, or subsection dedicated to artificial intelligence, according to Technology Transformation Service data portfolio lead Philip Ashlock. Ashlock was speaking at a Digital Government Institute conference on Thursday. The Data Federation is still in the very early stages, he said--long term, it's working with 18F and the Presidential Innovation Fellows program to develop a "maturity model" to understand how data projects tend to evolve.
There's still a long way to go before complex human traits like humor can be properly emulated by artificial intelligence, but Alphabet Inc. is already starting to inject wit into the research effort. The company last week published a machine learning model called "Parsey McParseface" that can automatically map out the linguist structure of any English-language text. The algorithm, which is hailed as the most accurate of its kind yet, was created using a neural networking system that became available on GitHub at the same time. Alphabet hopes that its contribution will ease the development of virtual assistants and other modern applications that deal with a lot of human-generated information. Equally importantly for the search giant, the move will also cement its position in the open-source machine learning community, which has emerged as a key focus area for the web-scale crowd.