Papers with Artificial agents
Concept-Best-Matching: Evaluating Compositionality In Emergent Communication (2024.findings-acl)
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| Challenge: | Existing evaluation methods do not expose compositionality of emergent communication . compositionality is a trait that enables the construction of complex meanings from the meaning of parts. |
| Approach: | They propose to find best-match between emergent words and natural language concepts to assess compositionality of emergentic communication. |
| Outcome: | The proposed algorithm provides a global score and translation-map between emergent words and natural language concepts. |
ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments (2022.coling-1)
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| Challenge: | embodied AI tasks require a strong understanding of verbs and their corresponding actions. |
| Approach: | They propose a controlled benchmark for embodied action understanding using a simulated environment and a visual feature extractor. |
| Outcome: | The proposed benchmark achieves 81.4% accuracy and high inter-annotator agreement . the proposed model falls behind human models in a zero-shot scenario . |
What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog. (P19-1)
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| Challenge: | a new study challenges the ability of artificial agents to engage in goal-oriented conversations . goal-orientated visual dialogue is a challenging task since it requires a strategy and contextual information to achieve a goal. |
| Approach: | They propose a goal-oriented visual dialogue system that combines reinforcement learning with regularized information gain. |
| Outcome: | The proposed system outperforms current state-of-the-art models on the GuessWhat?! dataset. |
Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games (2025.acl-long)
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| Challenge: | Text-based interactive environments have long presented formidable challenges for AI. |
| Approach: | They propose a method for projecting human personality traits onto agents to guide their behavior and integrate them into their policy-learning pipelines. |
| Outcome: | The proposed method induces personality in a text-based game agent by integrating personality profiles directly into the agent's policy-learning pipeline. |