Search Results for author: Jaan Aru

Found 8 papers, 3 papers with code

From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions

no code implementations29 Aug 2023 Mrugsen Nagsen Gopnarayan, Jaan Aru, Sebastian Gluth

Here, we argue that artificial intelligence (AI) research would benefit from a stronger focus on insights about how decisions emerge over time and incorporate related process data to improve AI predictions in general and human-AI interactions in particular.

Decision Making

The feasibility of artificial consciousness through the lens of neuroscience

no code implementations1 Jun 2023 Jaan Aru, Matthew Larkum, James M. Shine

Interactions with large language models have led to the suggestion that these models may soon be conscious.


Mind the gap: Challenges of deep learning approaches to Theory of Mind

no code implementations30 Mar 2022 Jaan Aru, Aqeel Labash, Oriol Corcoll, Raul Vicente

Theory of Mind is an essential ability of humans to infer the mental states of others.

Perspective Taking in Deep Reinforcement Learning Agents

no code implementations3 Jul 2019 Aqeel Labash, Jaan Aru, Tambet Matiisen, Ardi Tampuu, Raul Vicente

We believe that, in the long run, building better artificial agents with perspective taking ability can help us develop artificial intelligence that is more human-like and easier to communicate with.

reinforcement-learning Reinforcement Learning (RL)

APES: a Python toolbox for simulating reinforcement learning environments

2 code implementations31 Aug 2018 Aqeel Labash, Ardi Tampuu, Tambet Matiisen, Jaan Aru, Raul Vicente

Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years.

reinforcement-learning Reinforcement Learning (RL)

Do deep reinforcement learning agents model intentions?

1 code implementation15 May 2018 Tambet Matiisen, Aqeel Labash, Daniel Majoral, Jaan Aru, Raul Vicente

In this work we test whether deep reinforcement learning agents explicitly represent other agents' intentions (their specific aims or goals) during a task in which the agents had to coordinate the covering of different spots in a 2D environment.

reinforcement-learning Reinforcement Learning (RL)

What deep learning can tell us about higher cognitive functions like mindreading?

no code implementations28 Mar 2018 Jaan Aru, Raul Vicente

Can deep learning (DL) guide our understanding of computations happening in biological brain?

Object Recognition

Multiagent Cooperation and Competition with Deep Reinforcement Learning

4 code implementations27 Nov 2015 Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, Raul Vicente

In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong.

Q-Learning reinforcement-learning +1

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