Artificial Intelligence - Foundations of Computational Agents

By David Poole, Alan K. Mackworth. 2010

476 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. William Yang Wang
2. Kang Liu
3. Jun Zhao
4. Seyed Mehran Kazemi
5. Shizhu He
6. N. Schwalbe
7. Alan Jern
8. Najoung Kim

Citations in the corpus

• Multiagent cooperation and competition with deep reinforcement learning (2015, background): “This is the main intuition behind reinforcement learning [1, 2].”
• Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? (2018, background): “However, some AI experts have proposed that something ‘acts intelligently’ when: (1) what it does is appropriate for its circumstances and its goals; (2) it is flexible to changing environment and changing goals; (3) it learns from experience; and (4) it makes appropriate choices given its perceptua”
• Customer experiences in the age of artificial intelligence (2020, background): “More specifically, AI refers to “ computational agents that act intelligently ” (Poole and Mackworth, 2010, p. 3), designed to imitate the capability of human power while exceeding their ability for accuracy (Dwivedi et al., 2019).”
• Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning (2023, background): “Deduc-tive reasoning typically starts from known facts and rules, and iteratively makes new inferences until the goal statement can be proved or disproved (Poole and Mackworth, 2010).”
• Large Language Models are Better Reasoners with Self-Verification (2022, background): “In numerous decision-making tasks, humans often perform selfverification of inferred conclusions to mitigate mistakes (Poole and Mackworth, 2010).”
• A Canonical Theory of Dynamic Decision-Making (2012, background): “As discussed above it a major focus of current research on software agents (e.g., Wooldridge, 2000; Fox et al., 2003; Poole and Mackworth, 2010).”
• LAMBADA: Backward Chaining for Automated Reasoning in Natural Language (2022, background): “In the classic literature, there are two major approaches to logical reasoning (Poole and Mackworth, 2010):”
• A decision network account of reasoning about other people's choices. (2015, background): “…decision nets use a feature-based representation in which all of the features of the world and their relations to one another are represented explicitly by separate variables in a decision net (Boutilier, Dean, & Hanks, 1999; Boutilier, Dearden, & Goldszmidt, 2000; Poole & Mackworth, 2010).”
• On Defining Artificial Intelligence (2019, background): “In conferences, journals, and textbooks, “AI” has been interpreted in a very broad and loose manner, and consequently covers a wide variety of theories and approaches (Luger, 2008; Russell and Norvig, 2010; Poole and Mackworth, 2017).”
• Hybrid Intelligence (2019, background): “Other streams of research in this domain perceive AI as the ‘‘[ … ] synthesis and analysis of computational agents that act intelligently [ … ]’’ (Poole and Mackworth 2017, p. 3).”