A new polynomial-time algorithm for linear programming
Combinatorica
Formal theories of knowledge in AI and robotics
New Generation Computing
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Theoretical Computer Science - Thirteenth International Colloquim on Automata, Languages and Programming, Renne
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Readings in uncertain reasoning
Readings in uncertain reasoning
Handbook of theoretical computer science (vol. B)
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Representing plans under uncertainty: a logic of time, chance, and action
Representing plans under uncertainty: a logic of time, chance, and action
Artificial Intelligence
Probabilistic logic programming
Information and Computation
A situated view of representation and control
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
COBRA fundamentals and programming
COBRA fundamentals and programming
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Object identification: a Bayesian analysis with application to traffic surveillance
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Structured probabilistic models: Bayesian networks and beyond
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Heterogeneous active agents, I: semantics
Artificial Intelligence
Heterogeneous active agents, II: algorithms and complexity
Artificial Intelligence
The Semantics of Predicate Logic as a Programming Language
Journal of the ACM (JACM)
Heterogeneous active agents, III: polynomially implementable agents
Artificial Intelligence
Object Database Standard: ODMG-93
Object Database Standard: ODMG-93
Impact: A Platform for Collaborating Agents
IEEE Intelligent Systems
A Customizable Coordination Service for Autonomous Agents
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Formal Semantics for an Abstract Agent Programming Language
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Computationally fast Bayesian recognition of complex objects based on mutual algebraic invariants
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Foundations of a logic of knowledge, action, and communication
Foundations of a logic of knowledge, action, and communication
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Agents dealing with time and uncertainty
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
IMPACTing SHOP: Putting an AI Planner Into a Multi-Agent Environment
Annals of Mathematics and Artificial Intelligence
A Computational Logic Approach to Heterogenous Agent Systems
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Specifying Rational Agents with Statecharts and Utility Functions
RoboCup 2001: Robot Soccer World Cup V
Nonmonotonic reasoning: towards efficient calculi and implementations
Handbook of automated reasoning
Using methods of declarative logic programming for intelligent information agents
Theory and Practice of Logic Programming
Improving Performance of Heterogeneous Agents
Annals of Mathematics and Artificial Intelligence
Heterogeneous temporal probabilistic agents
ACM Transactions on Computational Logic (TOCL)
A stochastic language for modelling opponent agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Agent-oriented probabilistic logic programming
Journal of Computer Science and Technology - Special section on China AVS standard
Quantitative Disjunctive Logic Programming: semantics and computation
AI Communications
Reasoning about actions with sensing under qualitative and probabilistic uncertainty
ACM Transactions on Computational Logic (TOCL)
A hybrid plan recognition model for Alzheimer's patients: Interleaved-erroneous dilemma
Web Intelligence and Agent Systems
Quantitative logic programming revisited
FLOPS'08 Proceedings of the 9th international conference on Functional and logic programming
Temporal verification of probabilistic multi-agent systems
Pillars of computer science
Agent-Oriented probabilistic logic programming with fuzzy constraints
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
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Agents are small programs that autonomously take actions based on changes in their environment or “state”. Over the last few years, there has been an increasing number of efforts to build agents that can interact and/or collaborate with other agents. In one of these efforts Eiter et al. [1999] have shown how agents may be built on top of legacy code. However, their framework assumes that agent states are completely determined, and there is no uncertainty in an agent's state. Thus, their framework allows an agent developer to specify how his agents will react when the agent is 100% sure about what is true/false in the world state. In this paper, we propose the concept of a probabilistic agent program and show how, given an arbitrary program written in any imperative language, we may build a declarative “probabilistic” agent program on top of it which supports decision making in the presence of uncertainty. We provide two alternative semantics for probabilitic programs. We provide sound and complete algorithms to compute the semantics of positive agent programs.