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A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study.

AAMAS 2021

Sparse Training Theory for Scalable and Efficient Agents (Blue Sky Ideas Track)

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

technical paper

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This paper presents objective metrics for how explainable artificial intelligence (XAI) can be quantified. Through an overview of current trends, we show that many explanations are generated post-hoc and independent of the agent's logical process, which in turn creates explanations with limited meaning as they lack transparency and fidelity. While user studies are a known basis for evaluating XAI, studies that do not consider objective metrics for evaluating XAI may have limited meaning and may suffer from confirmation bias, particularly if they use low fidelity explanations unnecessarily. To avoid this issue, this paper suggests a paradigm shift in evaluating XAI that focuses on metrics that quantify the explanation itself and its appropriateness given the XAI goal. We suggest four such metrics based on performance differences, D, between the explanation's logic and the agent's actual performance, the number of rules, R, outputted by the explanation, the number of features, F, used to generate that explanation, and the stability, S, of the explanation. We believe that user studies that focus on these metrics in their evaluations are inherently more valid and should be integrated in future XAI research.

Better Metrics for Evaluating Explainable Artificial Intelligence (Blue Sky Ideas Track)

The demand for intelligent virtual advisors in our rapidly advancing world is rising and, consequently, the need for understanding the reasoning process to answer why a particular piece of advice is provided to the user is directly increasing. Personalized explanation is regarded as a reliable way to improve the user's understanding and trust in the virtual advisor. So far, cognitive explainable agents utilize reason explanation by referring to their own mental state (beliefs and goals) to explain their own behaviour. However, when the explainable agent plays the role of a virtual advisor and recommends a behaviour for the human to perform, it is best to refer to the user's mental state, rather than the agent’s mental state, to form a reason explanation. In this paper, we are developing an explainable virtual advisor (XVA) that communicates with the user to elicit the user's beliefs and goals and then tailors its advice and explains it according to the user's mental state. We tested the proposed XVA with university students where the XVA provides tips to reduce the students' study stress. We measured the impact of receiving three different patterns of tailored explanations (belief-based, goal-based, and belief&goal-based explanation) in terms of the students' intentions to change their behaviours. The results showed that the intention to change is not only related to the explanation pattern but also to the user context, the relationship built with the agent, the type of behaviour recommended and the user’s current intention to do the behaviour.

Reason Explanation for Encouraging Behaviour Change Intention

Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learning community. In particular, actor-critic methods with a centralized critic and decentralized actors are a common instance of this idea. However, the implications of using a centralized critic in this context are not fully discussed and understood even though it is the standard choice of many algorithms. We therefore formally analyze centralized and decentralized critic approaches, providing a deeper understanding of the implications of critic choice. Because our theory makes unrealistic assumptions, we also empirically compare the centralized and decentralized critic methods over a wide set of environments to validate our theories and to provide practical advice. We show that there exist misconceptions regarding centralized critics in the current literature and show that the centralized critic design is not strictly beneficial, but rather both centralized and decentralized critics have different pros and cons that should be taken into account by algorithm designers.

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