PapersFlow Research Brief
AI-based Problem Solving and Planning
Research Guide
What is AI-based Problem Solving and Planning?
AI-based Problem Solving and Planning is the cluster of artificial intelligence techniques and systems that enable reasoning, decision-making, and action sequencing under uncertainty, including planning systems, heuristic search, case-based reasoning, temporal planning, knowledge-based systems, robot control, model-based programming, probabilistic plan recognition, cognitive architecture, and autonomous systems.
This field encompasses 50,000 works focused on AI planning and reasoning methods. Key areas include probabilistic reasoning, temporal interval management, intelligent agents, and case-based reasoning. Growth rate over the past 5 years is not available in the provided data.
Topic Hierarchy
Research Sub-Topics
Classical Planning Heuristic Search
Develops admissible heuristics like LM-cut, pattern databases, and abstraction for optimal STRIPS planning. Researchers benchmark on IPC domains using downward, h_add, and merge-and-shrink techniques.
Temporal Planning PDDL
Extends PDDL+ for durative actions, continuous effects, and temporal constraints in scheduling and robotics. Focuses on temporal landmarks, mutex groups, and metric-FF adaptations.
Probabilistic Plan Recognition
Models agent goal inference from partial observations using POMDPs, Bayesian networks, and most likely explanation algorithms. Applications in security, assistive tech, and HRI.
Case-Based Reasoning Planning
Combines CBR cycles (retrieve, reuse, revise, retain) with HTN and partial-order planning for experience reuse. Studies adaptation knowledge and competency models.
Cognitive Architectures Planning
Integrates planning modules within SOAR, ACT-R, and CLARION for human-like deliberation, metacognition, and chunking. Evaluates psychological plausibility on Tower of Hanoi and logistics tasks.
Why It Matters
AI-based Problem Solving and Planning supports robot navigation in uncertain environments, as shown in "Probabilistic robotics" (2002) where planning algorithms use statistics from real-world data to guide robots around obstacles (7926 citations). Temporal planning relies on frameworks like those in "Maintaining knowledge about temporal intervals" (1983) by James F. Allen, which represent and reason over time intervals for scheduling and event-based systems (7498 citations). Intelligent agent design, detailed in "Intelligent agents: theory and practice" (1995) by Michael Wooldridge and Nicholas R. Jennings, applies to distributed systems for autonomous control (6520 citations). Case-based reasoning from "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches" (1994) by Agnar Aamodt and Enric Plaza enables reuse of past solutions in domains like diagnostics and robotics (5478 citations).
Reading Guide
Where to Start
"Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference" (1988) by Judea Pearl, as it provides foundational theory for reasoning under uncertainty cited in 16927 works and essential for understanding planning basics.
Key Papers Explained
"Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference" (1988) by Judea Pearl lays uncertainty foundations (16927 citations), extended to robotics in "Probabilistic robotics" (2002) by Sebastian Thrun (7926 citations). Temporal aspects build on "Maintaining knowledge about temporal intervals" (1983) by James F. Allen (7498 citations), informing agent theory in "Intelligent agents: theory and practice" (1995) by Michael Wooldridge and Nicholas R. Jennings (6520 citations). Case-based methods in "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches" (1994) by Agnar Aamodt and Enric Plaza (5478 citations) apply these in practical planning.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Current work builds on probabilistic and temporal foundations, but no recent preprints or news available; frontiers likely extend agent architectures and plan recognition to dynamic, multi-agent physical systems.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Probabilistic Reasoning in Intelligent Systems: Networks of Pl... | 1988 | — | 16.9K | ✕ |
| 2 | Bayesian Data Analysis | 1995 | — | 13.7K | ✕ |
| 3 | Induction of decision trees | 1986 | Machine Learning | 12.3K | ✓ |
| 4 | Philosophy in the flesh: the embodied mind and its challenge t... | 1999 | Choice Reviews Online | 9.9K | ✓ |
| 5 | Probabilistic robotics | 2002 | Communications of the ACM | 7.9K | ✕ |
| 6 | Maintaining knowledge about temporal intervals | 1983 | Communications of the ACM | 7.5K | ✓ |
| 7 | Intelligent agents: theory and practice | 1995 | The Knowledge Engineer... | 6.5K | ✓ |
| 8 | Case-Based Reasoning: Foundational Issues, Methodological Vari... | 1994 | AI Communications | 5.5K | ✕ |
| 9 | Active Learning Literature Survey | 2009 | Minds at UW (Universit... | 4.8K | ✓ |
| 10 | Intelligence without representation | 1991 | Artificial Intelligence | 4.6K | ✕ |
Frequently Asked Questions
What is probabilistic reasoning in AI planning?
Probabilistic reasoning provides theoretical foundations and computational methods for plausible inference under uncertainty. "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference" (1988) by Judea Pearl explicates probability as a language for reasoning with partial belief (16927 citations). These networks handle networks of plausible inference in intelligent systems.
How does temporal planning represent time intervals?
Temporal planning uses interval-based representations to maintain knowledge about time relations. "Maintaining knowledge about temporal intervals" (1983) by James F. Allen defines a set of 13 basic relations between time intervals, such as before, meets, overlaps, and during. This framework supports reasoning in scheduling and event planning systems.
What are intelligent agents in AI problem solving?
Intelligent agents are autonomous entities that perceive environments and act to achieve goals. "Intelligent agents: theory and practice" (1995) by Michael Wooldridge and Nicholas R. Jennings addresses theoretical and practical issues in agent design and construction. Agents integrate planning, reasoning, and interaction in AI systems (6520 citations).
What is case-based reasoning?
Case-based reasoning solves new problems by adapting solutions from similar past cases. "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches" (1994) by Agnar Aamodt and Enric Plaza outlines foundational issues, methodological variations, and system examples. It provides a general framework for CBR applications in planning and diagnostics (5478 citations).
How does planning apply to robotics?
Planning in robotics uses probabilistic methods to navigate uncertain environments. "Probabilistic robotics" (2002) by Sebastian Thrun exploits statistics from real-world sensor data for goal-directed movement and obstacle avoidance. These algorithms enable robust robot control in dynamic settings (7926 citations).
What role does heuristic search play in AI planning?
Heuristic search guides problem solving by estimating distances to goals in state spaces. The field includes methods like those building on decision tree induction in "Induction of decision trees" (1986) by J. R. Quinlan for efficient planning (12287 citations). Heuristics reduce search complexity in large planning problems.
Open Research Questions
- ? How can probabilistic planning integrate continuous-time representations beyond discrete intervals from Allen's framework?
- ? What methods combine case-based reasoning with heuristic search for scalable real-time planning in autonomous systems?
- ? How do cognitive architectures incorporate embodied intelligence without explicit representations, as in Brooks' approach?
- ? Which techniques improve probabilistic plan recognition in partially observable environments for robot control?
- ? How can knowledge-based systems handle uncertainty in temporal planning with high-dimensional state spaces?
Recent Trends
The field maintains 50,000 works with no specified 5-year growth rate.
No recent preprints from the last 6 months or news coverage in the past 12 months available, indicating reliance on established papers like Pearl (1988, 16927 citations) and Thrun (2002, 7926 citations).
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