Archive for the ‘Agents’ Category

Multiagent Systems

Monday, November 16th, 2015

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations by Yoav Shoham and Kevin Leyton-Brown.

From the webpage:

Multiagent systems consist of multiple autonomous entities having different information and/or diverging interests. This comprehensive introduction to the field offers a computer science perspective, but also draws on ideas from game theory, economics, operations research, logic, philosophy and linguistics. It will serve as a reference for researchers in each of these fields, and be used as a text for advanced undergraduate and graduate courses.

Emphasizing foundations, the authors offer a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, non-cooperative game theory, multiagent communication and learning, social choice, mechanism design, auctions, coalitional game theory, and logical theories of knowledge, belief, and other aspects of rational agency. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes, and mathematical programming.

Even better from the introduction:

Imagine a personal software agent engaging in electronic commerce on your behalf. Say the task of this agent is to track goods available for sale in various online venues over time, and to purchase some of them on your behalf for an attractive price. In order to be successful, your agent will need to embody your preferences for products, your budget, and in general your knowledge about the environment in which it will operate. Moreover, the agent will need to embody your knowledge of other similar agents with which it will interact (e.g., agents who might compete with it in an auction, or agents representing store owners)—including their own preferences and knowledge. A collection of such agents forms a multiagent system. The goal of this book is to bring under one roof a variety of ideas and techniques that provide foundations for modeling, reasoning about, and building multiagent systems.

Somewhat strangely for a book that purports to be rigorous, we will not give a precise definition of a multiagent system. The reason is that many competing, mutually inconsistent answers have been offered in the past. Indeed, even the seemingly simpler question—What is a (single) agent?—has resisted a definitive answer. For our purposes, the following loose definition will suffice: Multiagent systems are those systems that include multiple autonomous entities with either diverging information or diverging interests, or both.

This looks like a great item for a wish list this close to the holidays. Broad enough to keep your interest up and relevant enough to argue you are “working” and not just reading. 😉

ADMI2012: The Eighth International Workshop on Agents and Data Mining Interaction

Saturday, December 31st, 2011

ADMI2012: The Eighth International Workshop on Agents and Data Mining Interaction

Dates:

Electronic submission of full papers: February 28, 2012
Notification of paper acceptance: April. 10, 2012
Camera-ready copies of accepted papers: April 15, 2012
AAMAS-2012 workshop: June 4-5, 2012

From the Call for Papers:

The ADMI workshop provides a premier forum for sharing research and engineering results, as well as potential challenges and prospects encountered in the respective communities and the coupling between agents and data mining. The workshop welcomes theoretical work and applied dissemination aiming to:

  1. exploit agent-enriched data mining and demonstrate how intelligent agent technology can contribute to critical data mining problems in theory and practice;
  2. improve data mining-driven agents and show how data mining can strengthen agent intelligence in research and practical applications;
  3. explore the integration of agents and data mining towards a super-intelligent system;
  4. discuss existing results, new problems, challenges and impact of integration of agent and data mining technologies as applied to highly distributed heterogeneous, including mobile, systems operating in ubiquitous and P2P environments;
  5. identify challenges and directions for future research and development on the synergy between agents and data mining.

See the call for further details.

I almost forgot: JUNE 04-08, 2011 Valencia, Spain

Early summer, Spain? And a conference where subject identity (as in data mining) is going to be discussed? Not sure what more to ask for!

Siri’s Sibling Launches Intelligent Discovery Engine

Sunday, November 27th, 2011

Siri’s Sibling Launches Intelligent Discovery Engine

Completely unintentional but I ran across this article that concerns Siri as well:

We’re all familiar with the standard search engines such as Google and Yahoo, but there is a new technology on the scene that does more than just search the web – it discovers it.

Trapit, which is a personalized discovery engine for the web that’s powered by the same artificial intelligence technology behind Apple’s Siri, launched its public beta last week. Just like Siri, Trapit is a product of the $200 million CALO Project (Cognitive Assistant that Learns and Organizes), which was the largest artificial intelligence project in U.S. history, according to Mashable. This million-dollar project was funded by DARPA (Defense Advanced Research Projects Agency), the Department of Defense’s research arm.

Trapit, which was first unveiled in June, is a system that personalizes content for its users based on keywords, URLs and reading habits. This service, which can identify related content based on contextual data from more than 50,000 sources, provides a simple, carefree way to discover news articles, images, videos and other content on specific topics.

So, I put in keywords and Trapit uses those to return content to me, which if I then “trapit,” the system will continue to hunt for related content. Yawn. Stop me if you have heard this story before.

Keywords? That’s what we get from “…the largest artificial intelligence project in U.S. history?”

From Wikipedia on CALO:

Its five-year contract brought together 300+ researchers from 25 of the top university and commercial research institutions, with the goal of building a new generation of cognitive assistants that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise.

And we got keywords. Which Trapit uses to feed back similar content to us. I don’t need similar content, I need content that doesn’t use my keywords and yet is relevant to my query.

But rather than complain, why not build a topic map system based upon “…cognitive assistants that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise.” Err. that would be crowdsourcing topic map authoring, yes?

Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions

Tuesday, November 22nd, 2011

Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions by Adi Shklarsh, Gil Ariel, Elad Schneidman, and Eshel Ben-Jacob, appeared in September 2011 Issue of PLoS Computational Biology.

I mentioned to Jack Park that I had been thinking about swarms and mining semantics and he forwarded a link to the ScienceDaily article, Smart Swarms of Bacteria Inspire Robotics: Adaptable Decision-Making Found in Bacteria Communities, which was an adaptation of the PLoS Computational Biology article I cite above.

Abstract (from the PLoS article):

Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment – by decreasing the peers’ influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.

This research has a number of aspects that are relevant to semantic domains.

First, although bacteria move in “complex terrains,” those terrains are no more complex and probably less so than the semantic terrains that are presented to agents (whether artificial or not). Whatever we can learn about navigation mechanisms that are successful for other, possibly less complex terrains, will be useful for semantic terrains.

Second, the notion of “performance” as increasing or decreasing influence over other agents sounds remarkably similar to “boosting” except that “boosting” is crude when compared to the mechanisms discussed in this paper.

Third, rather than complex and possibly rigid/fragile modeling (read ontologies, description logic), perhaps simpler computations in agents with short memories may be more successful.

No proof, just airy speculation at this point but experimental proof, the 19th century logicists may concede, is the best kind.