Category Archives: Cognitive Sciences

On how the exploitation-exploration dicotomy shifts to exploitation as humans get older

September 25, 2023 15:05 ,

R. Nathan Spreng, Gary R. Turner, From exploration to exploitation: a shifting mental mode in late life development, Trends in Cognitive Sciences, Volume 25, Issue 12, 2021 DOI: 10.1016/j.tics.2021.09.0010.

Changes in cognition, affect, and brain function combine to promote a shift in the nature of mentation in older adulthood, favoring exploitation of prior knowledge over exploratory search as the starting point for thought and action. Age-related exploitation biases result from the accumulation of prior knowledge, reduced cognitive control, and a shift toward affective goals. These are accompanied by changes in cortical networks, as well as attention and reward circuits. By incorporating these factors into a unified account, the exploration-to-exploitation shift offers an integrative model of cognitive, affective, and brain aging. Here, we review evidence for this model, identify determinants and consequences, and survey the challenges and opportunities posed by an exploitation-biased mental mode in later life.

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Analysis of the under-optimality of path lengths when path planning is carried out on a grid instead of the continuous world

September 15, 2023 10:15 ,

James P. Bailey, Alex Nash, Craig A. Tovey, Sven Koenig, Path-length analysis for grid-based path planning, Artificial Intelligence, Volume 301, 2021, DOI: 10.1016/j.artint.2021.103560.

In video games and robotics, one often discretizes a continuous 2D environment into a regular grid with blocked and unblocked cells and then finds shortest paths for the agents on the resulting grid graph. Shortest grid paths, of course, are not necessarily true shortest paths in the continuous 2D environment. In this article, we therefore study how much longer a shortest grid path can be than a corresponding true shortest path on all regular grids with blocked and unblocked cells that tessellate continuous 2D environments. We study 5 different vertex connectivities that result from both different tessellations and different definitions of the neighbors of a vertex. Our path-length analysis yields either tight or asymptotically tight worst-case bounds in a unified framework. Our results show that the percentage by which a shortest grid path can be longer than a corresponding true shortest path decreases as the vertex connectivity increases. Our path-length analysis is topical because it determines the largest path-length reduction possible for any-angle path-planning algorithms (and thus their benefit), a class of path-planning algorithms in artificial intelligence and robotics that has become popular.

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Building explanations for AI plans by modifying user’s models to make those plans optimal within them

September 15, 2023 10:11 ,

Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati, Foundations of explanations as model reconciliation, Artificial Intelligence, Volume 301,
2021, DOI: 10.1016/j.artint.2021.103558.

Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where users have domain and task models that differ from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a \u201cmodel reconciliation problem\u201d (MRP), where the AI system in effect suggests changes to the user’s mental model so as to make its plan be optimal with respect to that changed user model. We will study the properties of such explanations, present algorithms for automatically computing them, discuss relevant extensions to the basic framework, and evaluate the performance of the proposed algorithms both empirically and through controlled user studies.

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On how physical movements shape the perception of time

September 15, 2023 09:28 ,

Rose De Kock, Keri Anne Gladhill, Minaz Numa Ali, Wilsaan Mychal Joiner, Martin Wiener, How movements shape the perception of time, Trends in Cognitive Sciences, Volume 25, Issue 11, 2021, Pages 950-963 DOI: 10.1016/j.tics.2021.08.002.

In order to keep up with a changing environment, mobile organisms must be capable of deciding both where and when to move. This precision necessitates a strong sense of time, as otherwise we would fail in many of our movement goals. Yet, despite this intrinsic link, only recently have researchers begun to understand how these two features interact. Primarily, two effects have been observed: movements can bias time estimates, but they can also make them more precise. Here we review this literature and propose that both effects can be explained by a Bayesian cue combination framework, in which movement itself affords the most precise representation of time, which can influence perception in either feedforward or active sensing modes.

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Safety in MDPs by measuring the probability of reaching dangerous states

July 21, 2023 09:54 ,

Rafal Wisniewski, Luminita-Manuela Bujorianu, Safety of stochastic systems: An analytic and computational approach, . Automatica, Volume 133, 2021 DOI: 10.1016/j.automatica.2021.109839.

We refine the concept of stochastic reach avoidance for a general class of Markov processes introducing a threshold of p for the reaching probability. This new problem is called p-safety, and it aims to ensure that the given process reaches a forbidden set before leaving its ‘working’ state space with a probability of less than p. In the situation when an initial probability measure characterizes the initial states, a variant of p-safety is put forward. We call this form of safety weak p-safety. In this work, we characterize both p-safety and weak p-safety and show how to compute them. We employ semi-definite programming to compute p-safety and linear programming to compute weak p-safety. To get to this point, we use certificates of positivity of polynomials translated into the sum of squares and the Bernstein forms.

Posted in: Artificial Intelligence , Tagged: ,

Solving the “self-recognition on a mirror” problem for robots

July 21, 2023 09:49 ,

Arianna Pipitone, Antonio Chella, Robot passes the mirror test by inner speech, . Robotics and Autonomous Systems, Volume 144, 2021 DOI: 10.1016/j.robot.2021.103838.

The mirror test is a well-known task in Robotics. The existing strategies are based on kinesthetic-visual matching techniques and manipulate perceptual and motion data. The proposed work attempts to demonstrate that it is possible to implement a robust robotic self-recognition method by the inner speech, i.e. the self-dialogue that enables reasoning on symbolic information. The robot self-talks and conceptually reasons on the symbolic forms of signals, and infers if the robot it sees in the mirror is itself or not. The idea is supported by the existing literature in psychology, where the importance of inner speech in self-reflection and self-concept emergence for solving the mirror test was empirically demonstrated.

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Trying to reach general AI through just decision-making (rewards) instead of using a diversity of paradigms

July 21, 2023 08:37 ,

avid Silver, Satinder Singh, Doina Precup, Richard S. Sutton, Reward is enough, . Artificial Intelligence, Volume 299, 2021 DOI: 10.1016/j.artint.2021.103535.

In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.

NOTES:

  • The computational and physical limitations of the agent to cope with a too complex world is the main reason to use learning instead of pre-built knowledge (evolution): it allows the agent to focus on acquiring skills for its own circumstances first, that are the most important for it.
  • Argument why classification (supervised learning) is less powerful and efficient than RL.
  • Same with multi-agent settings vs. one agent confronted with a single complex environment (containing other agents).
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A nice survey on active learning, in particular for robotics

July 17, 2023 09:10 ,

Annalisa T. Taylor, Thomas A. Berrueta, Todd D. Murphey, Active learning in robotics: A review of control principles, . Mechatronics, Volume 77, 2021 DOI: 10.1016/j.mechatronics.2021.102576.

Active learning is a decision-making process. In both abstract and physical settings, active learning demands
both analysis and action. This is a review of active learning in robotics, focusing on methods amenable to
the demands of embodied learning systems. Robots must be able to learn efficiently and flexibly through
continuous online deployment. This poses a distinct set of control-oriented challenges??one must choose
suitable measures as objectives, synthesize real-time control, and produce analyses that guarantee performance
and safety with limited knowledge of the environment or robot itself. In this work, we survey the fundamental
components of robotic active learning systems. We discuss classes of learning tasks that robots typically
encounter, measures with which they gauge the information content of observations, and algorithms for
generating action plans. Moreover, we provide a variety of examples ?? from environmental mapping to
nonparametric shape estimation ?? that highlight the qualitative differences between learning tasks, information
measures, and control techniques. We conclude with a discussion of control-oriented open challenges, including
safety-constrained learning and distributed learning.

NOTES:

  • RL can be considered one of the areas within computational learning theory, that usually ignore physical embodiment aspects of the learning agent. However, that is only so when RL explores through decision-making, not when it explores randomly, without much purpose of enhancing learning itself through its actions.
  • RL caveats (particularly Deep RL): their large data requirements, lack of generalizability between tasks, as well as their inability to learn incrementally and guarantee
    safety.
  • Bayesian filters can be seen as learner systems: they learn parameters of objects (pose) or environments (maps) aided by some models. However, they are more active learners when they use the robot actions to improve that parameter learning.
  • Gaussian processes can be effective in learning those models when no parameterical form is available or much first-principle knowledge, for instance, when the robot has to learn the model only observing a small part of the environment (local).
  • Entropy/information, Fisher’s information (conditional information) and ergodicity are the main ways of measuring information gain in active learning.
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Example of non-NN approach that produces better results in classification tasks than NNs

July 17, 2023 08:44 ,

Jiang, Zhiying and Yang, Matthew and Tsirlin, Mikhail and Tang, Raphael and Dai, Yiqin and Lin, Jimmy, Low-Resource Text Classification: A Parameter-Free Classification Method with Compressors, . Findings of the Association for Computational Linguistics: ACL 2023 URL.

Deep neural networks (DNNs) are often used for text classification due to their high accuracy. However, DNNs can be computationally intensive, requiring millions of parameters and large amounts of labeled data, which can make them expensive to use, to optimize, and to transfer to out-of-distribution (OOD) cases in practice. In this paper, we propose a non-parametric alternative to DNNs that??s easy, lightweight, and universal in text classification: a combination of a simple compressor like gzip with a k-nearest-neighbor classifier. Without any training parameters, our method achieves results that are competitive with non-pretrained deep learning methods on six in-distribution datasets.It even outperforms BERT on all five OOD datasets, including four low-resource languages. Our method also excels in the few-shot setting, where labeled data are too scarce to train DNNs effectively.

Posted in: Artificial Intelligence , Tagged: ,

The Evolutionary History of Brains for Numbers

July 11, 2023 09:43 ,

Andreas Nieder, The Evolutionary History of Brains for Numbers, . Trends in Cognitive Sciences, Volume 25, Issue 7, 2021, Pages 608-621 DOI: 10.1016/j.tics.2021.03.012.

Humans and other animals share a number sense’, an intuitive understanding of countable quantities. Having evolved independent from one another for hundreds of millions of years, the brains of these diverse species, including monkeys, crows, zebrafishes, bees, and squids, differ radically. However, in all vertebrates investigated, the pallium of the telencephalon has been implicated in number processing. This suggests that properties of the telencephalon make it ideally suited to host number representations that evolved by convergent evolution as a result of common selection pressures. In addition, promising candidate regions in the brains of invertebrates, such as insects, spiders, and cephalopods, can be identified, opening the possibility of even deeper commonalities for number sense.

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