Knowledge representation and scalable abstract reasoning for simulated democracy in Unity
We present a novel form of scalable knowledge representation about agents in a simulated democracy, e-polis, where real users respond to social challenges associated with democratic institutions, structured as Smart Spatial Types, a new type of Smart Building that changes architectural form according to the philosophical doctrine of a visitor. At the end of the game players vote on the Smart City that results from their collective choices. Our approach uses deductive systems in an unusual way: by integrating a model of democracy with a model of a Smart City we are able to prove quality aspects of the simulated democracy in different urban and social settings, while adding ease and flexibility to the development. Second, we can infer and reason with abstract knowledge, which is a limitation of the Unity platform; third, our system enables real-time decision-making and adaptation of the game flow based on the player's abstract state, paving the road to explainability. Scalability is achieved by maintaining a dual-layer knowledge representation mechanism for reasoning about the simulated democracy that functions in a similar way to a two-level cache. The lower layer knows about the current state of the game by continually processing a high rate of events produced by the in-built physics engine of the Unity platform, e.g., it knows of the position of a player in space, in terms of his coordinates x,y,z as well as their choices for each challenge. The higher layer knows of easily-retrievable, user-defined abstract knowledge about current and historical states, e.g., it knows of the political doctrine of a Smart Spatial Type, a player's philosophical doctrine, and the collective philosophical doctrine of a community players with respect to current social issues.
Code (0)
등록된 구현이 없습니다.
Tasks
UnitySimilar Papers 제목 키워드 기반
Slot Abstractors: Toward Scalable Abstract Visual Reasoning
Abstract visual reasoning is a characteristically human ability, allowing the identification of relational patterns that are abstracted away from object features, and the systematic generalization of those patterns to un…
ObjectSystematic GeneralizationVisual ReasoningSymbolic and Abstractive Reasoning with Complex Visual Queries
Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data type termed complex visual query (CVQ), …
Knowledge GraphsVisual ReasoningAbstract Activation Spaces for Content-Invariant Reasoning in Large Language Models
Large Language Models (LLMs) often struggle with deductive judgment in syllogistic reasoning, systematically conflating semantic plausibility with formal validity a phenomenon known as content effect. This bias persists …
Cross-Lingual TransferKaVa: Latent Reasoning via Compressed KV-Cache Distillation
Large Language Models (LLMs) excel at multi-step reasoning problems with explicit chain-of-thought (CoT), but verbose traces incur significant computational costs and memory overhead, and often carry redundant, stylistic…
Self-supervised Hierarchical Visual Reasoning with World Model
3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are essential in such settings. While existing …
Reinforcement LearningVisual Reasoning