Reasoning Agent

A Reasoning Agent is an autonomous cognitive artificial agent that can perform reasoning tasks through logical inference, planning, and decision-making processes.

• AKA: Rational Agent, Reasoning System, Cognitive Reasoning Agent, Deliberative Agent, Intelligent Reasoning Agent.
• Context:
  • It can typically perform Logical Reasoning through deductive reasoning, inductive reasoning, and abductive reasoning.
  • It can typically implement Decision-Making Processes through belief-desire-intention architectures and utility-based reasoning.
  • It can typically execute Planning Tasks through goal-directed behavior and sequential decision-making.
  • It can typically maintain Knowledge Representations through symbolic representations, semantic networks, and ontological models.
  • It can typically support Learning Capability through experience-based learning and knowledge acquisition.
  • ...
  • It can often utilize Cognitive Architectures such as ACT-R architecture, Soar architecture, or CoALA framework.
  • It can often integrate Memory Components including working memory, long-term memory, and episodic memory.
  • It can often employ Reasoning Frameworks such as chain-of-thought reasoning, tree-of-thoughts reasoning, or ReAct framework.
  • It can often demonstrate Metacognitive Capability through self-reflection, self-monitoring, and reasoning strategy adaptation.
  • ...
  • It can range from being a Simple Reasoning Agent to being a Complex Reasoning Agent, depending on its reasoning capability sophistication.
  • It can range from being a Reactive Reasoning Agent to being a Deliberative Reasoning Agent, depending on its reasoning processing depth.
  • It can range from being a Domain-Specific Reasoning Agent to being a General-Purpose Reasoning Agent, depending on its reasoning application scope.
  • It can range from being a Symbolic Reasoning Agent to being a Hybrid Reasoning Agent, depending on its reasoning architecture type.
  • ...
  • It can interact with External Environments through perception systems and action execution mechanisms.
  • It can collaborate with Other Agents through multi-agent reasoning and distributed problem-solving.
  • It can handle Uncertainty through probabilistic reasoning and fuzzy logic reasoning.
  • It can process Natural Language through language understanding modules and semantic reasoning.
  • It can maintain Goal Hierarchy through intention management and plan monitoring.
  • ...
• Example(s):
  • Cognitive Architecture-Based Reasoning Agents, such as:
    • ACT-R-Based Reasoning Agents implementing production rule systems for cognitive modeling tasks.
    • Soar-Based Reasoning Agents utilizing chunking mechanisms for problem-space search tasks.
    • CoALA-Based Language Agents employing modular memory architectures for language reasoning tasks.
  • Domain-Specific Reasoning Agents, such as:
    • Medical Diagnosis Reasoning Agents performing diagnostic reasoning for clinical decision support.
    • Legal Reasoning Agents conducting case-based reasoning for legal analysis tasks.
    • Financial Reasoning Agents executing risk assessment reasoning for investment decision tasks.
  • Autonomous System Reasoning Agents, such as:
    • Autonomous Vehicle Reasoning Agents performing real-time decision-making for navigation tasks.
    • Robotic Reasoning Agents implementing spatial reasoning for manipulation tasks.
    • UAV Control Reasoning Agents conducting tactical reasoning for mission planning tasks.
  • AI Model-Based Reasoning Agents, such as:
    • LLM-Based Reasoning Agents utilizing transformer architectures for complex reasoning tasks.
    • Neuro-Symbolic Reasoning Agents combining neural networks with symbolic reasoning systems.
    • Reinforcement Learning Reasoning Agents employing reward-based learning for sequential decision tasks.
  • Game-Playing Reasoning Agents, such as:
    • Chess-Playing Reasoning Agents using minimax algorithms for strategic reasoning.
    • AlphaGo-Style Reasoning Agents implementing Monte Carlo tree search for game strategy reasoning.
    • Poker-Playing Reasoning Agents utilizing counterfactual regret minimization for imperfect information games.
  • Scientific Reasoning Agents, such as:
    • Hypothesis Generation Agents performing scientific reasoning for research tasks.
    • Theorem Proving Agents conducting mathematical reasoning for formal verification.
    • Data Analysis Reasoning Agents executing statistical reasoning for pattern discovery tasks.
  • ...
• Counter-Example(s):
  • Reactive Agents, which lack deliberative reasoning capability and respond only to stimuli.
  • Scripted Agents, which follow predetermined sequences without adaptive reasoning.
  • Reflex Agents, which use condition-action rules without internal state representation.
  • Random Agents, which make arbitrary decisions without logical reasoning processes.
  • Instinctual Agents, which rely on hardcoded behaviors rather than reasoning mechanisms.
• See: Reasoning, Cognitive Architecture, Intelligent Agent, Rational Choice Theory, Belief-Desire-Intention Architecture, Planning System, Knowledge-Based System, Multi-Agent System, Artificial General Intelligence, Symbolic AI, Machine Reasoning, Automated Reasoning.

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References

2022

• (Irshad et al., 2022) ⇒ M.Z. Irshad, N.C. Mithun, Z. Seymour, and others. (2022). “Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous 3D Environments.” In: 2022 26th.
  • NOTE: It presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions.

2020

• (Velástegui, 2020) ⇒ P.G. Velástegui. (2020). “The Reasoning Agent: Agency in the Capability Approach and Some Implications for Development Research and Practice.” In: Revista Iberoamericana de Estudios de.
  • NOTE: It focuses on the concept of a reasoning agent in the capability approach, highlighting its importance for inquiry, including the philosophy of science.

2017

• (Lindner & Bentzen, 2017) ⇒ F. Lindner, and M.M. Bentzen. (2017). “The Hybrid Ethical Reasoning Agent IMMANUEL.” In: Proceedings of the Companion of the 2017.
  • NOTE: It introduces a software library that supports the implementation of hybrid ethical reasoning agents (HERA), aiming to make moral principles accessible to robots.

2017

• (Floyd et al., 2017) ⇒ M.W. Floyd, J. Karneeb, P. Moore, and D.W. Aha. (2017). “A Goal Reasoning Agent for Controlling UAVs in Beyond-Visual-Range Air Combat.” In: IJCAI.
  • NOTE: It details a significant improvement in controlling UAVs in beyond-visual-range air combat using a goal reasoning agent, highlighting successes over scripted agents.

2011

• (Kahneman, 2011) ⇒ Daniel Kahneman. (2011). “Thinking, Fast and Slow." Macmillan. ISBN:0374533555
  • QUOTE: ... Bruno Frey barely recalls writing the piece, but I can still recite its first sentence: “The agent of economic theory is rational, selfish, and his tastes do not change.” ...

2003

• (Haarslev & Möller, 2003) ⇒ Volker Haarslev, and Ralf Möller. (2003). “Racer: An owl reasoning agent for the semantic web.” In: Proceedings of the International Workshop on Applications, Products and Services of Web-based Support Systems.
• (Haarslev & Möller, 2003) ⇒ Volker Haarslev, and Ralf Möller. (2003). “Racer: A Core Inference Engine for the Semantic Web." In EON, vol. 87.

1996

• (Franklin & Graesser, 1996) ⇒ Stan Franklin, and Art Graesser. (1996). “Is It An Agent, Or Just a Program?: A Taxonomy for Autonomous Agents.” In: Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages.
  • QUOTE: A Hayes-Roth Agent reasons to interpret perceptions, solve problems, draw inferences, and determine actions, i.e., is a reasoning agent.

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