An Interleaving Semantics of the Timed Concurrent Language for
Argumentation to Model Debates and Dialogue Games
- URL: http://arxiv.org/abs/2306.07675v2
- Date: Fri, 7 Jul 2023 07:37:54 GMT
- Title: An Interleaving Semantics of the Timed Concurrent Language for
Argumentation to Model Debates and Dialogue Games
- Authors: Stefano Bistarelli, Maria Chiara Meo, Carlo Taticchi
- Abstract summary: We propose a language for modelling concurrent interaction between agents.
Such a language exploits a shared memory used by the agents to communicate and reason on the acceptability of their beliefs.
We show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Time is a crucial factor in modelling dynamic behaviours of intelligent
agents: activities have a determined temporal duration in a real-world
environment, and previous actions influence agents' behaviour. In this paper,
we propose a language for modelling concurrent interaction between agents that
also allows the specification of temporal intervals in which particular actions
occur. Such a language exploits a timed version of Abstract Argumentation
Frameworks to realise a shared memory used by the agents to communicate and
reason on the acceptability of their beliefs with respect to a given time
interval. An interleaving model on a single processor is used for basic
computation steps, with maximum parallelism for time elapsing. Following this
approach, only one of the enabled agents is executed at each moment. To
demonstrate the capabilities of language, we also show how it can be used to
model interactions such as debates and dialogue games taking place between
intelligent agents. Lastly, we present an implementation of the language that
can be accessed via a web interface. Under consideration in Theory and Practice
of Logic Programming (TPLP).
Related papers
- Asynchronous Tool Usage for Real-Time Agents [61.3041983544042]
We introduce asynchronous AI agents capable of parallel processing and real-time tool-use.
Our key contribution is an event-driven finite-state machine architecture for agent execution and prompting.
This work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions.
arXiv Detail & Related papers (2024-10-28T23:57:19Z) - Hello Again! LLM-powered Personalized Agent for Long-term Dialogue [63.65128176360345]
We introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent)
It incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation.
The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated.
arXiv Detail & Related papers (2024-06-09T21:58:32Z) - Modeling Real-Time Interactive Conversations as Timed Diarized Transcripts [11.067252960486272]
We present a simple yet general method to simulate real-time interactive conversations using pretrained language models.
We demonstrate the promise of this method with two case studies: instant messenger dialogues and spoken conversations.
arXiv Detail & Related papers (2024-05-21T21:14:31Z) - Dialogue-based generation of self-driving simulation scenarios using
Large Language Models [14.86435467709869]
Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars.
Current simulation frameworks are driven by highly-specialist domain specific languages.
There is often a gap between a concise English utterance and the executable code that captures the user's intent.
arXiv Detail & Related papers (2023-10-26T13:07:01Z) - Explaining Interactions Between Text Spans [50.70253702800355]
Reasoning over spans of tokens from different parts of the input is essential for natural language understanding.
We introduce SpanEx, a dataset of human span interaction explanations for two NLU tasks: NLI and FC.
We then investigate the decision-making processes of multiple fine-tuned large language models in terms of the employed connections between spans.
arXiv Detail & Related papers (2023-10-20T13:52:37Z) - Generative Agents: Interactive Simulacra of Human Behavior [86.1026716646289]
We introduce generative agents--computational software agents that simulate believable human behavior.
We describe an architecture that extends a large language model to store a complete record of the agent's experiences.
We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims.
arXiv Detail & Related papers (2023-04-07T01:55:19Z) - What A Situated Language-Using Agent Must be Able to Do: A Top-Down
Analysis [16.726800816202033]
Even in our increasingly text-intensive times, the primary site of language use is situated, co-present interaction.
This paper attempts a top-down analysis of what the demands are that unrestricted situated interaction makes on the participating agent.
arXiv Detail & Related papers (2023-02-16T21:30:26Z) - Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs.
We employ domain-adaptive training strategies to help the model adapt to the dialogue domains.
Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
arXiv Detail & Related papers (2023-01-10T13:18:25Z) - A Model for Intelligible Interaction Between Agents That Predict and Explain [1.335664823620186]
We formalise the interaction model by taking agents to be automata with some special characteristics.
We define One- and Two-Way Intelligibility as properties that emerge at run-time by execution of the protocol.
We demonstrate using the formal model to: (a) identify instances of One- and Two-Way Intelligibility in literature reports on humans interacting with ML systems providing logic-based explanations, as is done in Inductive Logic Programming (ILP); and (b) map interactions between humans and machines in an elaborate natural-language based dialogue-model to One- or Two-Way Intellig
arXiv Detail & Related papers (2023-01-04T20:48:22Z) - CloneBot: Personalized Dialogue-Response Predictions [0.0]
The project task was to create a model that, given a speaker ID, chat history, and an utterance query, can predict the response utterance in a conversation.
The model is personalized for each speaker. This task can be a useful tool for building speech bots that talk in a human-like manner in a live conversation.
arXiv Detail & Related papers (2021-03-31T01:15:37Z) - TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented
Dialogue [113.45485470103762]
In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling.
To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
arXiv Detail & Related papers (2020-04-15T04:09:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.