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Open access journal Intelligent Computing, published in affiliation with Zhejiang Lab, publishes the latest research outcomes and technological breakthroughs in intelligent computing.
Led by Shiqiang Zhu (Zhejiang Lab) and Ninghui Sun (Institute of Computing Technology, CAS), Intelligent Computing's editorial board is comprised of leading experts in the field of intelligent computing.
Intelligent Computing is now seeking submissions to special issues on:
- Graph Computing
- Cognitive Capacities for Large-Scale Distributed Systems
- Computational Imaging
- Deep Learning for Cross-Media Analysis and Knowledge Discovery
- Knowledge Graphs
- Photonic Computing
Latest ArticlesMore articles
Global-to-Local Design for Self-Organized Task Allocation in Swarms
Programming robot swarms is hard because system requirements are formulated at the swarm level (i.e., globally) while control rules need to be coded at the individual robot level (i.e., locally). Connecting global to local levels or vice versa through mathematical modeling to predict the system behavior is generally assumed to be the grand challenge of swarm robotics. We propose to approach this problem by programming directly at the swarm level. Key to this solution is the use of heterogeneous swarms that combine appropriate subsets of agents whose hard-coded agent behaviors have known global effects. Our novel global-to-local design methodology allows to compose heterogeneous swarms for the example application of self-organized task allocation. We define a large but finite number of local agent controllers and focus on the global dynamics of behaviorally heterogeneous swarms. The user inputs the desired global task allocation for the swarm as a stationary probability distribution of agents allocated over tasks. We provide a generic method that implements the desired swarm behavior by mathematically deriving appropriate compositions of heterogeneous swarms that approximate these global user requirements. We investigate our methodology over several task allocation scenarios and validate our results with multiagent simulations. The proposed global-to-local design methodology is not limited to task allocation problems and can pave the way to formal approaches to design other swarm behaviors.
What Is Missing from Contemporary AI? The World
In the past three years, we have witnessed the emergence of a new class of artificial intelligence systems–—so-called foundation models, which are characterised by very large machine learning models (with tens or hundreds of billions of parameters) trained using extremely large and broad data sets. Foundation models, it is argued, have competence in a broad range of tasks, which can be specialised for specific applications. Large language models, of which GPT-3 is perhaps the best known, are the most prominent example of current foundation models. While foundation models have demonstrated impressive capabilities in certain tasks—natural language generation being the most obvious example—I argue that because they are inherently disembodied, and they are limited with respect to what they have learned and what they can do. Foundation models are likely to be very useful in many applications: but they are not the end of the road in artificial intelligence.