Theory / Part I: |
Introduction: Anticipation in Natural and Artificial Cognition / Giovanni Pezzulo ; Martin V. Butz ; Cristiano Castelfranchi ; Rino Falcone1: |
Introduction / 1.1: |
The Path to Anticipatory Cognitive Systems / 1.2: |
Symbolic Behavior, Representation-Less Behavior, and Their Merge to Anticipatory Behavior / 1.2.1: |
The Power of Anticipation: From Reactivity to Proactivity / 1.2.2: |
The Anticipatory Approach to Cognitive Systems / 1.2.3: |
The Unitary Nature of Anticipation / 1.2.4: |
Anticipation in Living Organisms / 1.3: |
Anticipatory Natural Cognition / 1.3.1: |
Anticipatory Codes in the Brain / 1.3.2: |
Simulative Theories of Cognition, and Their Unifying Nature / 1.3.3: |
Conclusions / 1.4: |
The Anticipatory Approach: Definitions and Taxonomies / 2: |
Anticipatory Systems, Anticipation, and Anticipatory Behavior / 2.1: |
Prediction vs. Anticipation / 2.2: |
Predictive Capabilities / 2.2.1: |
Anticipatory Capabilities / 2.2.2: |
Anticipation and Goal-Oriented Behavior / 2.3: |
The Anticipatory Structure of Goal-Oriented Behavior / 2.3.1: |
Not All Anticipatory Behavior Is Goal-Oriented / 2.3.2: |
Which Anticipations Permit Goal-Oriented Action? / 2.3.3: |
The Hierarchical Organization of Anticipatory Goal-Oriented Action / 2.3.4: |
Additional Elements of True Goal-Oriented Behavior / 2.3.5: |
Anticipation and Learning / 2.4: |
Learning to Predict / 2.4.1: |
Bootstrapping Autonomous Cognitive Development: Surprise and Curiosity / 2.4.2: |
From Willed to Automatic Control of Action and Vice Versa on the Basis of Surprise / 2.4.3: |
Benefits of Anticipations in Cognitive Agents / 2.5: |
Potentials for Anticipatory Systems / 3.1: |
Potential Benefits of Anticipatory Mechanisms on Cognitive Functions / 3.2: |
Effective, Context-Based Action Initiation / 3.2.1: |
Faster and Smoother Behavior Execution / 3.2.2: |
Improving Top-Down Attention / 3.2.3: |
Improving Information Seeking / 3.2.4: |
Improving Decision Making / 3.2.5: |
Object Grounding, Categorization, and Ontologies / 3.2.6: |
Social Abilities / 3.2.7: |
Learning / 3.2.8: |
Arising Challenges Due to Anticipations and Avoiding Them / 3.3: |
Conclusion / 3.4: |
Models, Architectures, and Applications / Part II: |
Anticipation in Attention / Christian Balkenius ; Alexander Forster ; Birger Johansson ; Vin Thorsteinsdottir4: |
Learning What to Look at / 4.1: |
A Learning Saliency Map / 4.2.1: |
Cue-Target Learning / 4.3: |
Cueing by a Single Stimulus / 4.3.1: |
Contextual Cueing / 4.3.2: |
Fovea Based Solution / 4.3.3: |
Attending to Moving Targets / 4.4: |
Models of Smooth Pursuit / 4.4.1: |
Engineering Approaches / 4.4.2: |
The State Based Approach / 4.4.3: |
The Prediction Approach / 4.4.4: |
The Fovea Based Approach / 4.4.5: |
Combining Bottom-Up and Top-Down Processes / 4.5: |
Anticipatory, Goal-Directed Behavior / Oliver Herbort5: |
A Brief History of Schemas / 5.1: |
Schema Approaches / 5.2: |
Symbolic Schemas for Policy Learning / 5.2.1: |
Symbolic Schemas and Prediction for Selection / 5.2.2: |
Neural-Based Planning / 5.2.3: |
Neural Network-Based Dynamic Programming / 5.2.4: |
Inverse Model Approaches / 5.3: |
Inverse Models in Motor Learning and Control / 5.3.1: |
Inverse Models and Schema Approaches / 5.3.2: |
Advanced Structures / 5.4: |
Prediction and Action / 5.4.1: |
Coupled Forward-Inverse Models / 5.4.2: |
Hierarchical Anticipatory Systems / 5.4.3: |
Evaluation of Predictive and Anticipatory Capabilities / 5.5: |
Schema-Based Systems / 5.5.1: |
Discussion / 5.5.2: |
Contrasting Predictive System Capabilities / 5.6.1: |
Contrasting Anticipatory System Capabilities / 5.6.2: |
Integration / 5.6.3: |
Anticipation and Believability / Carlos Martinho ; Ana Paiva5.7: |
Animation and Believability / 6.1: |
Emotion and Exaggeration / 6.1.2: |
Anticipation / 6.1.3: |
Anticipation, Emotion, and Believability / 6.1.4: |
Related Work / 6.2: |
Oz Project / 6.2.1: |
EMA / 6.2.2: |
Duncan the Highland Terrier / 6.2.3: |
Emotivector / 6.3: |
Architecture / 6.3.1: |
Anticipation Model / 6.3.2: |
Salience Model / 6.3.3: |
Sensation Model / 6.3.4: |
Selection Model / 6.3.5: |
Uncertainty / 6.3.6: |
Aini, the Synthetic Flower / 6.4: |
Emotivectors in Action / 6.4.1: |
Evaluation / 6.4.2: |
iCat, the Affective Game Buddy / 6.5: |
Emotivector Integration in Agent Architectures / 6.5.1: |
Anticipation and Emotions for Goal Directed Agents / Emiliano Lorini ; Michele Piunti ; Maria Miceli6.7: |
Related Works in Affective Computing / 7.1: |
Expectations and Surprise / 7.3: |
A Typology of Expectations and Predictions / 7.3.1: |
From the Typology of Expectations to the Typology of Surprise / 7.3.2: |
Roles of Surprise in Cognitive Processing / 7.3.3: |
Expectations and Emotions for Goal-Directed Agents / 7.4: |
Expectations and Decision Making / 7.4.1: |
Situated Agents and Affective States / 7.4.2: |
Confidence of Predictions and Modulation of the Probability Function / 7.4.3: |
A Reinforcement-Learning Model of Top-Down Attention Based on a Potential-Action Map / Dimitri Ognibene ; Gianluca Baldassarre7.4.4: |
Methods / 8.1: |
RGB Camera Input / 8.2.1: |
Saliency Map and Action Selection / 8.2.2: |
Fovea / 8.2.3: |
Periphery Map / 8.2.4: |
Inhibition-of-Return Map / 8.2.5: |
Potential Action Map / 8.2.6: |
Actor-Critic Model / 8.2.7: |
Parameter Settings / 8.2.8: |
The Tasks / 8.2.9: |
Results / 8.3: |
Learning and Performance of the Models / 8.3.1: |
Bottom-Up Attention: Periphery Map and Inhibition-of-Return Map / 8.3.2: |
Analysis of the Vote Maps / 8.3.3: |
Capability of Learning to Stay, and of Staying, on the Target / 8.3.4: |
Potential Action Map: An Action-Oriented Memory of Cue Information / 8.3.5: |
Potential Action Map: Capacity to Integrate Multiple Sources of Information / 8.3.6: |
Anticipation by Analogy / Boicho Kokinov ; Maurice Grinberg ; Georgi Petkov ; Kiril Kiryazov8.4: |
The Anticipation by Analogy Scenario / 9.1: |
Models of Analogy-Making / 9.3: |
AMBR Model of Analogy-Making / 9.4: |
Integrating Visual Perception and Motor Control in AMBR / 9.5: |
Top-Down Perception / 9.5.1: |
Attention / 9.5.2: |
Transfer of the Solution / 9.5.3: |
Action Execution / 9.5.4: |
Running the Simulated Model and Comparing It with Human Data / 9.6: |
Comparing with Human Data / 9.6.1: |
Running the Real Robot Model in the Real World / 9.7: |
Ikaros / 9.7.1: |
AMBR2Robot / 9.7.2: |
Tests / 9.7.3: |
Mechanisms for Active Vision / 9.8: |
Discussion and Conclusion / 9.9: |
Anticipation in Coordination / Emilian Lalev10: |
The Prisoner's Dilemma Game / 10.1: |
Related Research / 10.2: |
Fictitious Play / 10.2.1: |
Strategic Teaching and Reputation Formation / 10.2.2: |
Social Order and Coordination / 10.2.3: |
Anticipation and Information Processing in Societies / 10.2.4: |
Agent Architecture and Decision Making Model / 10.3: |
The Model / 10.3.1: |
Judgment and Decision Making / 10.3.2: |
Game Simulations with Individual Agents: Comparison with Experimental Results / 10.4: |
Comparison of the Model with Experimental Results / 10.4.1: |
Multi-Agent Simulations / 10.5: |
Agent Societies / 10.5.1: |
Simulation Results and Discussions / 10.5.2: |
Endowing Artificial Systems with Anticipatory Capabilities: Success Cases / 10.6: |
Flexible Goal-Directed Arm Control: The SURE_REACH Architecture / 11.1: |
Learning Cognitive Maps for Anticipatory Control: Time Growing Neural Gas / 11.3: |
Learning Effective Directional Arm Control: The Evolutionary System XCSF / 11.4: |
Anticipatory Target Motion Prediction / 11.5: |
Anticipatory Spatial Attention with Saliency Maps / 11.6: |
Behavior Prediction in a Group of Robots / 11.7: |
Enhanced Adaptivity in a Predator-Prey Scenario / 11.8: |
Adaptive Navigation and Control with Anticipation / 11.9: |
Mental Experiments for Selecting Actions / 11.10: |
Anticipations for Believable Behavior / 11.11: |
Anticipatory Behavior in a Searching-for-an-Object Task / 11.12: |
The Role of Anticipation in Cooperation and Coordination / 11.13: |
Anticipatory Effects of Expectations and Emotions / 11.14: |
On-Line and Off-Line Anticipation for Action Control / 11.15: |
References / 11.16: |
Theory / Part I: |
Introduction: Anticipation in Natural and Artificial Cognition / Giovanni Pezzulo ; Martin V. Butz ; Cristiano Castelfranchi ; Rino Falcone1: |
Introduction / 1.1: |